{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n",
      "Index(['pregnants', 'Plasma_glucose_concentration', 'blood_pressure',\n",
      "       'Triceps_skin_fold_thickness', 'serum_insulin', 'BMI',\n",
      "       'Diabetes_pedigree_function', 'Age', 'Target'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1300x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"E:\\\\Jupyter\\\\pima-indians-diabetes.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['pregnants', 'Plasma_glucose_concentration', 'blood_pressure',\n",
      "       'Triceps_skin_fold_thickness', 'serum_insulin', 'BMI',\n",
      "       'Diabetes_pedigree_function', 'Age', 'Target'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "print((df[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.Target);\n",
    "plt.xlabel('target');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dc2ece8c18>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"pregnants\",hue=\"Target\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.pregnants);\n",
    "plt.xlabel('pregnants');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0qce5c3uYPXsSNWtWZsaMifZvTAKIDAp9UgECnNyzEnkzzComOvwfdKT5rTp88UZSFy8AQBqvImTu3JT8f88g21fdydCyDtkGdk203BPCrcBQsno+ab+rhythT7UfoGTVUrTt055x3ccQ9ejJx5Y06dIwdMYI5v80l/M+5xIl5yRD7jESSdTTX088Xn78la0BCLdUbB7/FLHhuLEHkZsAJ6VUaszVprZa6xLAn0DqePYx8Yyp4LXW3wE9MFeb9j8e1vdUzFStdTmtdbkeXTrakKp4Wad8zpA7Xy5y5PbAydmJRi3rsW3TLpv2dfPIRqrUqQDIkDE9pSuUxO/SNXumaxe+x86SO19OPC3noEHLOmzfvNumfYf2HkXjcm1oUr4tE0ZPZu2Sjfzy7e92zvjVHTt6irz53yTXmzlwdnamRZtGbNqwzSpm04ZttO/YEoCmLeqze+cBwPxhuUp18/1GadKmoWy5Uly8cBmA8ZO+4cL5y/wxeVYitubl+Rw9Sb78b5Lb0v6WrRuzaf3fVjGb1v9N+3fN7W/WsgG7d+6P2aaUolnLhqyM1TGaNW0hpQpXp3zJOjRv2InLF/1o3bRL4jToPzh0+BgFCuQlT55cODs70759C9astZ5gZc3azbz3nnl2sjZtmrBt+574DhXDPyCIIkUKkjWruXJct251zp5NmpMQHD583Kr97do1Y+1a68lV1q71pnNn8+xkrVs3Zvv2vQDkyZMrZrKF3LlzULBgfq5evc7XX39PgQIVKVSoCl269GH79r106/ZZ4jbsP4o4eR6XPJ4453QDZycyNKnOva37rWKM2Z4Mo0xXpyKPLl0HIHDgj1yq2ZVLtbsR8t007q7cSshPMxMz/Vd24fgFPPJ6kj2XG07OTlRtVp1D3getYvIWy8cn43oztvs33Ln1ZPiwk7MTX/05lO3L/2bvuuf/jojkQ/6OUfKXWyn1ttZ6H9AR2I15GBwAWuu7SqkrSql2WusllnuCSmqtjwP7gTaYq0vv2PBcjztBoZZKVFtg6Qv2+QeIuY9IKZVfa30SOKmUehsoDJx91s6OMmjEdxzyOUF4+F3qtOxMr+7v0eapG5STM5PJxNjBP/HHwp8xGg2sWLCWS+eu0PuLnvgeP8v2Tbso7lWEiTO+J0Om9NSsX5Xeg3rSssa75CuYl0GjPkVrjVKKmVPmceHMJUc36aWZTCa+HzKB3xaMx2A0smrBWi6fu8InX/Tg9LGz7Ni8m6JehRk/fRwZMqWner0qfDyoB21rJO3pqJ/HZDIxZNC3LFj2J0ajgYVzV3D+7EUGDenDcR9fNm/YxoI5y/j1j+/Ze3Qj4bfD+fiDgQDM+GsBEyd/y/Z9q1FKsXDeCs74nqdCpTK0e6cFp33P4b3LXDQeN3oif3vvdGRT42UymRg88BsWLp+G0WhgwdxlnDt7kS+G9OW4zyk2bdjG/DlLmTT1B/b7bCL89h0++uDJVOxvVylPYEAQV/1uOLAVr8ZkMtHvs2GsXzcfo8HAzFmLOH36PCNHDOTwkeOsXevN9BkLmTXzF86e3s3t2+G82/nJlNYXz+8nQ4Z0uLi40KJ5Qxo16ciZMxf4ZswEtv29nMjISK5d8+eD7p87sJXPZjKZ+Oyzr1mzZg5Go5FZsxZx5sx5hg/vz5EjJ1m3zpuZMxcxffpEfH13EhYWTpcufQCoXLk8Awf2IjIykujoaPr1G8qtW8+upCULpmiCR08h17QxYDRwZ+lmHl28RtZPOxNx6gL3/j5Ali4tSFe7ItpkwhT+D4FfjXd01gkm2hTNn1//zog5ozAYDWxdtIXr56/RsX8nLp68wCHvg7w/tBup06Zm0JSvAAgJCGFc9zFUaVqVohWKkT5Temq3NU/i8suAifidvuLIJiWeZHY/na2UjIdMvpRSeYD1wE7Mw+UuAO8Bp4FyWutQS1xeYArmIXTOwEKt9WilVEHMw+8UsA74UGudQylVExiotW5q2X8ScFhrPVMpNQZzJ8oPuA5c1VqPVEptt+xzWCmV1RKfRymVBdhked5xmIf61cJcVToNdNVaW09xFEtk6OXX/gItXexdR6fgMM4On1TR8YIikvkHr1cUnUL/uvrLuPXgH0en4FBOBnkdOJa7uKNTcKjBEXINrLi2JklNAfvQd6vdP5+lKlYn0dssFaPkL1pr/fTNDXliL2itrwAN49nXH6iktdZKqXeAw5b47cD2WPv3ifV4GDDs6QNprWvGehz6OAetdRhQPlboohe2SAghhBBCJF0p9Esr6Ri93soCkyzD68KBDxycjxBCCCGEEA4hHaNkTGvtB/zn+rrWeheQ/KYTE0IIIYQQjpNC7zGSWemEEEIIIYQQrz2pGAkhhBBCCCFsprXJ0SnYhVSMhBBCCCGEEK89qRgJIYQQQgghbJdCZ6WTipEQQgghhBDitScVIyGEEEIIIYTtZFY6IYQQQgghhEiZpGIkhBBCCCGEsF0KvcdIOkZCCCGEEEII20XLdN1CCCGEEEIIkSJJxUgIIYQQQghhuxQ6lE4qRkIIIYQQQojXnlSMhBBCCCGEELaT6bqFEEIIIYQQImWSipEQQgghhBDCdnKPkRBCCCGEEEKkTFIxEkIIIYQQQthO7jESQgghhBBCiJRJKkZCCCGEEEII20nFSAghhBBCCCFSJqkYCZHE+fjOd3QKDlW5ZFdHp+BQEVGPHJ2CQ/XNWtHRKTjcyYx3HZ2CQ11+dMvRKTjcD4/SOjoFh/rkoXxcTWq0Njk6BbuQK00kaaWLvevoFBzqde8UCSGEEEIkFukYCSGEEEIIIWwn9xgJIYQQQgghRMokFSMhhBBCCCGE7bRUjIQQQgghhBAiRZKKkRBCCCGEEMJ2co+REEIIIYQQQqRMUjESQgghhBBC2C6F3mMkHSMhhBBCCCGE7WQonRBCCCGEEEKkTFIxEkIIIYQQQtguhQ6lk4qREEIIIYQQ4rUnFSMhhBBCCCGE7eQeIyGEEEIIIYRImaRiJIQQQgghhLCdVIyEEEIIIYQQImWSipEQQgghhBDCdjIrnRBCCCGEEEKkTFIxEkIIIYQQQthO7jESQgghhBBCiJRJKkZCCCGEEEII28k9RkIIIYQQQgiRMknFSLy2qtSqxFdjPsdoNLBs3mqm/TrHanvZSl58+c3nvFU0P4M++hrvtdsA8MjpzsTp32E0GnBycmL+tCUsnr3CEU2wm2Fjx7Nzz0GyZM7Eyrm/Ozodu3m7ZgUGfPMpBoOBVQvWMWvSPKvtpSuWov/ovhQoko+hn4zi73U7Yrbtv76NS2cvAxDkf5MBXQcnau7/VZ261Rn3wzCMRiNzZi1m4vg/rLa7uLgw5c8f8fIqTljYbT54vx/Xr/mTK3cODhzZxMUL5jYfPnSM/v2Gky7dG6zfvCBmf88c7ixeuIohX36bqO36rwrUKEnj4e+hjAaOLtrOrilrrLaX61SHiu/VIzo6mkf/RrB68DRCLvqTo1Q+mo/rAYBSsG3ics5sOuyIJryS0jXK0H1kTwxGA1sWerP8t6VW25v3aEHdjvUxRZm4G3aXSQN/JsQ/hDxF8/Lxt71Ikz4t0SYTSyctZs+a3Q5qxaupXKsiX37zGQajkRXz1jB9kvV7QZlKXnwxuh8Fi+bny49HsMXyXvDYG+nSsnLXAv7esINxQ8YnZuoJongNL94d3g1lNLBr0VbWT1lptb1+96ZUf6cOpqho/gm7y4wvJnPLPxSALJ5Z6frdJ2TxdAWtmdBtLLduhDiiGf+Za61SFB7zPspo4Ma8v/H7dXW8cW5NK1Jq2ufsrz+Eu8cv496mCnl6NYvZnr5obvbXHcw/vlcTK3XHSqH3GEnHSLyWDAYDw74bSM/2nxIUcJNFm2awbdMuLp/3i4kJ9A9mWL9v6PrJu1b7hgSH0rlpTyIfRZImbRpW7pjPtk27CAkOTeRW2E/LxvV4t01zhnzzk6NTsRuDwcAXYz+nzzv9CQ4MYdb6qezctJsrF568qQX5BzPqs7F0/vidOPs/jHhIp3rdEzPlV2YwGPhx/EhaNX+fAP8g/t65nA3rt3Lu7MWYmPfeb8ed8DuULVWH1m2bMPKbL+j+fj8A/K5co3rl5lbHvHfvX6t123atZO3qzYnToFekDIqmo7syq/M47gaF8dHqbzjrfZSQi/4xMSdX7eXwvK0AFKpbhoZfd2LO+z9w89wN/mg2jGhTNOmyZaLXhrGc23KUaFPy+bBgMBj4cMzHjOz0NbcCb/HDmvEc9D7AjQvXY2Iu+15mYJP+PIp4SIPOjegypBv/6/0Djx485OfPxxPoF0hmtyz8tG4CPjt8uH/3Xwe26OUZDAaGjBvIR+37ERx4k/kbp7F9s/V7QZB/EF/3G8P7vd6N9xi9v/yQw/t8EinjhKUMBjqP7sH/Oo8mLCiM4au/45j3YQIu3oiJuXb6CqObfcmjiEfU7FyfdoPf4/c+EwDoMb4vayct4/TuE6RKmxqd3D4sGxRFvvuAI+2/JSLgFpU2jSVk0xH+Pe9vFWZ8IzW5ezQk/MiFmHVBy/YQtGwPAOmK5MJr1sDXp1OUgr1wKJ1SyqSUOqaUOqWUWqKUSmtZf8/+6b0apdRMpVRbR+eRlCmluiqlPP/Dfi2VUkVjLY9WStVN2Ozsp0SZoly7coMbVwOIioxiw0pvajesbhUTcD2Q86cvEh2trdZHRUYR+SgSAJdUzhgMKtHyTizlvEqQMUN6R6dhV8VKF+G6nz/+1wKJiozCe9VWajSoahUTeCOIi2cuo5+6BpKrsuVKcfnyVa76XScyMpLlS9fRuIn1r22jJnVZMM9cAV21YiM1ar5t8/Hz5X+TbNlc2bvnUILmbS85vfITdjWY29dDMEWaOLlmP4Xrl7WKeXjvQcxjl7SpwHIpREY8iukEOaVyjlmfnBT0KkigXyDB14KJioxi95qdVKhf0Srm1L6TPIp4CMB5n3O4ergCEHAlgEC/QABuB4dxJ/QOGbNkSNwGJIDipYty/coN/K+Z3ws2rtxCzQbVrGICrgdx4cwlouP50F+kZCFcs2Vh346DiZVygsrnVYCbV4MIuX4TU2QUB9bswat+eauYs/t8eRTxCIDLPhfI7G6+BjwL5MRoNHB69wkAHt6PiIlLLjKWKcD9K0E8uHoTHWkiaOVesjcsFyeuwFftuTJ5DdERkfEex71VFYJW7LV3ukmLjrb/jwPYco/RA621l9a6OPAI+NjOOYnE1RWIt2OklDI+Z7+WQEzHSGs9XGu9JWFTs5/s7tkICrgZsxwccJPs7tls3t/dMzvLt81ly9HVTJs0J0VVi14X2dyzEhz7GggMIZuH7deASyoXZm2YyvQ1U6jRsOqLd0gCPDzd8L8RGLMc4B+Eh6ebVYxnrBiTycTdO/fI4poZgNxv5mTHntWs3TiftyvH/fDQpl0zli9bZ8cWJKz0blm4E3ArZvluYBgZ3DLHiavwXj0+2zGe+l91ZN3IWTHrc3rlp8/m7+m96TvWDJuerKpFAFncXQkNePLadSvwFq5urs+Mr9uhHke3HYmzvmCpgjg7OxF0NcguedpTdo9sBAUExyzfDAzBzcbXAaUUA0b2ZfzoSfZKz+4yuWUhLNY1cDvwFpndsjwzvlr72pzcbq6OueXz4P7d+/T+fRAj1v1Iu8HvoQzJ69b11O5ZiIj1GhAREEYqd+v2py+eh9SeroR6H33mcdxbvE3Qij12y1Mknpe9gncBBWKvUEqlU0ptVUodVUqdVEq1sKx/Qym1Til13FJt6mBZ76eUGquU2qeUOqyUKqOU2qSUuqSU+vh5x3wWpdTXSqmzSilvpdQCpdTAeGL8lFJZLY/LKaW2x3quGZbnOaGUamNZ39Gy7pRS6nvLOqOlCnXKsu1zy/r8SqmNSqkjSqldSqnCz8nVTSm1wnJejiulKlvW97cc95RS6jPLujxKqTNKqT+VUr5Kqc1KqTSWbQWUUlssxziqlMpvWT9IKXXI0pZRzzuOpZpWDphnqQqmsZyn4Uqp3UA7pVRPy/GOK6WWKaXSWnJuDvxo2S9/7OqcUqqOUsrHco6mK6VSxfo/GBXr/zXe86SU+tBybRwOe3AzvpBXplTcKs/LfOEbFHCT1rU607hSW1p0aIxrtme/kYikKd5pnIRKAAAgAElEQVRrQNt+FTQr3473G33I171H039UX3K8+dKF10RnU5ufERMcFEKJItWpUaU5Q7/6lj+nTyB9+nRWca3bNmXZkjVx9k+q4mlqvNfAwTneTKzRn83fLaRG35Yx628cu8Sk+l/yR/OvqfZJc3PlKBl5md+BGq1qkr9kAVb+sdxqfebsmek3sT+/Dvz5pX5/kgpbr4H4dOjWmt1b91l9wZLcvMw1UKllNfKUzM/GqasAMBiNFCxfmMXfzuKb5l+SLbcbVdvWtGe6CS/eAR+x2q8UhUZ34dzIuc88RMYyBTA9eMi9szeeGZMiRUfb/8cBbO4YKaWcgEbAyac2RQCttNZlgFrA/5T5N60hEKC1LmWpNm2Mtc91rfXbmDtaM4G2QCVg9AuOGV9e5YA2QGmgNeYP+i/ja+CO1rqE1rok8LcyDy37HqgNeAHllVItLY9zaK2La61LADMsx5gK9NValwUGAr895/l+AXZorUsBZQBfpVRZoBtQ0XIeeiqlSlviCwKTtdbFgHBLWwHmWdaXAioDgUqp+pb4CpZcyyqlqj/rOFrrpcBhoJOlKvh4zEiE1rqq1nohsFxrXd7yPGeA7lrrvcBqYJBlv0uPG6eUSo35/7SD5Rw5AZ/Ean+o5f91iuVcxaG1nqq1Lqe1LpclTfbnnMr/LjjwJu6eT47t5pmdkKCXv2E0JDiUi2evUKZiqYRMTySCm4EhuMW+BjyyERpke+UvNNj8LaP/tUCO7j1GoeIFEzzHhBbgH0SOnB4xy5453AkKvPnMGKPRSIaM6bgdFs6jR4+4HRYOwPFjvly5co38BfLE7Fe8eGGcjEaOH/O1f0MSyN2gMDJ6PqmQZPDIwj83w58Zf2rNPorUi/sWE3opgMgHD8n+Vk675GkvtwJDyeqZNWbZ1cOVsJthceJKVi1F2z7tGdd9DFGPomLWp0mXhqEzRjD/p7mc9zmXKDkntOCAENxjVU2ze2Tjpo2vAyXLFuedbm1Yf2gZ/Yf3oWm7RvQb+smLd0xCbgfdIkusayCzhyvhN2/HiStapQRN+7Thlx7fxVwDt4Nuce20HyHXbxJtisZn80HeLJ4v0XJPCBGBYaSO9RqQ2jMLD4OetN8pXWrSFc5J+eXDqXboVzKWLYDX7IFkKPWkne4tK79+w+hSMFs6RmmUUscwf4C+Bkx7arsCxiqlTgBbgByAG+YOVF2l1PdKqWpa6zux9nk85cdJ4IDW+h+tdQgQoZTK9JxjxqcqsEpr/UBr/Q/wsl9X1gUmP17QWt8GygPbtdYhWusozJ2Q6sBlIJ9S6lelVEPgrlIqHeaOyRLLefoD8Hj6SWKpjblTgNbaZDkvVYEVWut/tdb3gOXA40HOV7TWxyyPjwB5lFLpMXfQVliOE6G1vg/Ut/z4AEeBwpg7RPEe5zk5Lor1uLilCnYS6AQUe85+AIUsz3XesjwL87l77PHXjS/Kwa5O+Zwhd75c5MjtgZOzE41a1mPbpl027evmkY1UqVMBkCFjekpXKInfpWv2TFfYweljZ8mdNyeeuczXQL0Wddi52bahEOkzpsPZxVwdyJglIyXLl+BKrJu1k6qjR06QP/+b5H4zJ87OzrRu24QN67daxWxcv5WOnVoB0KJVQ3bu2A+Aa9YsGCzDZN7Mk4t8+d/Ez+/JTfpt2jVj2dK1idSShOF//DJZ8riTKWc2jM5GSjSrxFlv66FiWfI8eet5q7YXt/zMw8Uy5cyGwWg+HxlzZMU1nwfhyWw2rgvHL+CR15PsudxwcnaiarPqHPK2vlcmb7F8fDKuN2O7f8OdW0/exp2cnfjqz6FsX/43e9cl3yFEvsfOkDtfzpj3goYt67Jjs22z6w3pPYqG5VrTuHwbxo+exNolG/j52yl2zjhhXTl+Ebc8HmTNmR2jsxMVm1XhmLf1PYK5i+Wly9iP+KXHd/xz626sfS/xRsY3SG+5t6xI5eIEXEheVZO7PpdIm8+dNLmzoZyNuLeszM1NT14Dov55wPaiH7KrfF92le/LnSMXOdblJ+4eN8/OiVK4NatI0MrXsGOUQitGtsxK90Br7fWc7Z2AbEBZrXWkUsoPSK21Pm+phDQGximlNmutH1eEHlr+jY71+PGy07OO+Yznt/XO9yiedARjH0sRdxRV/MVVrW8rpUoBDYDeQHvgMyD8BefoRZ7XhtjnxwSkeU68AsZpra3m31VK5XnGcZ4l9rRCM4GWWuvjSqmuQM3n7Pc4h+d5nIcJB86KaDKZGDv4J/5Y+DNGo4EVC9Zy6dwVen/RE9/jZ9m+aRfFvYowccb3ZMiUnpr1q9J7UE9a1niXfAXzMmjUp2itUUoxc8o8Lpy59OInTUYGjfiOQz4nCA+/S52WnenV/T3aNGvg6LQSlMlk4oehE/ll/k8YjQZWL1zP5fN+fDToA84cP8fOzXsoWqowP0wbQ4ZM6alarzIfDfyADrXeJ2/BPAz+fiDR0dEYDAZmTZ5nNZtdUmUymfhiwCiWrZyB0Whk3pwlnD1zgcHD+nHs6Ck2rN/KnFmL+f2v/3Hk+FZu3w6ne9fPAKhcpTyDh32GKSoKkymaAf2GE377yQfllq0b0b5ND0c17T+JNkWzbvhMusz+EoPRwNHFOwi54E/tz9vgf/IK57YcpeL79clfpTimKBMRd/5l+QDz9PVvli9EtU+aYYoyoaOjWfv1DO7fTvJzElmJNkXz59e/M2LOKAxGA1sXbeH6+Wt07N+JiycvcMj7IO8P7UbqtKkZNOUrAEICQhjXfQxVmlalaIVipM+Untpt6wDwy4CJ+J2+4sgmvTSTycS4IeOZsmACBqORlZb3gl5f9MD32Fl2bN5NMa8iTJg+jgyZ0lOjXlV6DepO6xqdHZ16gog2RTN3+F/0nz0Mg9HA7sV/E3DhBi0/74DfyUsc23KY9oPfI1Xa1PT6bQAAt/xD+bXn9+joaBZ9O5uB80agFPidusyOhcnmVmMAtCmas4NnUGbhEJTRgP+Cbfx77gb5v2jH3eOXCdkU95662DK/XYSIwDAeXE2+wymFNfWisbRKqXta63TPWq+U6gcU0Fr3VUrVAv4G8mKeqCFMax1hGYbWVWvd0tLJKae1DrV80C6nte5jOaYf5qFwneI7ptbaL548ymOu0lTG/EH7CPCn1vonpdRMYK3WeqlSagvwP631BqXUBKC01rqmUuo7zB25x/f1ZMbccdoPlAVuA5uAX4E9wCOt9V2llBcwU2vtpZTaC0zQWi+xDPkrqbU+/ozzuRDYr7WeqMyTG7yB+b6tmZiH0SngAPCe5bnXWoYiosz3TqXTWo9USu0HvtNar7Tcw2PEXHn6Bqijtb6nlMoBRAJpn3OcNcB4rfW22P8HWutQy3Io5kkWbgPrAX+tdVel1K/AUa31DEvcTGCt5ec8UFtrfdGy3kdr/fNT//flgJ+01jXjO0+PFXerlPwGrScgH9/5jk7B4SqX7OroFBzq4t0AR6fgUH2zVnxxUAp3Mvrui4NSsMuPbr04KIUrmzrp38NoT+88kL8uUz94YZKaAvfBolF2/3yWpsOIRG9zQkwfMg8op5Q6jLlDc9ayvgRw0DK8bCgwJgGOGYfW+hDmoXnHMQ/TOgzciSd0FPCzUmoX5mrFY2OAzMo86cFxoJbWOhAYDGyzHPeo1noV5iF92y1tmmmJwZJjd8v+vsDzJovoB9SyDE07AhTTWh+1HO8g5k7RX1rrF/1RhPeATy3DDfcC7lrrzcB8YJ/l+EuBF825PBP4/fHkC/Fs/9qSkzfW/w8LgUGWSRbyP16ptY7AfL/UEksO0UDK/QuhQgghhBCvmxQ6lO6FFaPkQCmVzlIhSQvsBD60dDZEMicVI6kYScVIKkavO6kYScVIKkZSMUpyFaMFI+xfMeo4KtHbnFKutKnK/MdGUwOzpFMkhBBCCCGEnTioomNvyaZjpJRyBbbGs6mO1vrdxM7nRZRSQ4F2T61eorX+1hH5CCGEEEIIIZ4t2XSMtNa3MP9tnmTB0gGSTpAQQgghhEhZdMqsGCXE5AtCCCGEEEIIkawlm4qREEIIIYQQIglIofcYScVICCGEEEII8dqTipEQQgghhBDCdingz/3ERypGQgghhBBCiNeedIyEEEIIIYQQtouOtv+PDZRSDZVS55RSF5VSX8WzPbdSaptSykcpdUIp1fh5x5OOkRBCCCGEECJZUUoZgclAI6Ao0FEpVfSpsGHAYq11aeAd4LfnHVPuMRJCCCGEEELYLmnMSlcBuKi1vgyglFoItABOx4rRQAbL44xAwPMOKB0jIYQQQgghRHKTA7gea/kGUPGpmJHAZqVUX+ANoO7zDihD6YQQQgghhBC209F2/1FKfaiUOhzr58OnslDxZfbUckdgptY6J9AYmKOUemb/RypGQgghhBBCiCRFaz0VmPqckBtArljLOYk7VK470NByvH1KqdRAVuBmfAeUipEQQgghhBDCZjpa2/3HBoeAgkqpvEopF8yTK6x+KuYaUAdAKVUESA2EPOuA0jESQgghhBBCJCta6yigD7AJOIN59jlfpdRopVRzS9gAoKdS6jiwAOiq9bP/Oq0MpRNCCCGEEELYLmnMSofWej2w/ql1w2M9Pg1UsfV4UjESQgghhBBCvPakYiSEEEIIIYSwnU4aFaOEJh0jIYQQQgghhO1smxwh2ZGOkUjSnJXR0SkIB9t7YqajU3C4FmX6ODoFh5l+97ijU3C4ZhmKOjoFh9p897l/qP61UDhVdken4FDvPjjh6BQcLtTRCbwmpGMkRBJWuWRXR6fgUNIpEkIIIZKgJDL5QkKTyReEEEIIIYQQrz2pGAkhhBBCCCFsJxUjIYQQQgghhEiZpGIkhBBCCCGEsJ1OmbPSScVICCGEEEII8dqTipEQQgghhBDCdnKPkRBCCCGEEEKkTFIxEkIIIYQQQtguWu4xEkIIIYQQQogUSSpGQgghhBBCCNtpucdICCGEEEIIIVIkqRgJIYQQQgghbCf3GAkhhBBCCCFEyiQVIyGEEEIIIYTNtPwdIyGEEEIIIYRImaRiJIQQQgghhLCd3GMkhBBCCCGEECmTVIyEEEIIIYQQtkuhf8dIOkZCCCGEEEII28lQOiGEEEIIIYRImaRiJIQQQgghhLCdTNctRMpSuVZFVuxewKp9i+jWp3Oc7WUqlWL+5ukcurGDuk1rxtn+Rrq0bPJZyZdj+ydCtgnv7ZoVWLprLsv3zOf9Pp3ibC9dsRRzNv3Fvmt/U7tJDatt+69vY573NOZ5T+N/M8clVsqJatjY8VRv8g4tO3/s6FTspmyNskzdNpW/dv5Fu17t4mxv1aMVv2/9ncmbJjN2wViy58ges2307NEsPrmYkTNGJmLGr65mnSrsOLCG3YfX07tf9zjbXVyc+W3aT+w+vJ413vPJmcsTACcnJyZM/pYtu5ezbf9qen/WI2af7h91ZsueFWzdu5LuH8d9LUnKitYoxcitExm1/Rfqf9IizvY63Zsw3Hs8Qzf8SL95X5MlR9aYba2+6sTXm//H8C3jaT+iW2Km/Urq1auBz7GtnDi5nQEDPomz3cXFhVmzJ3Hi5Ha271hJ7tw5rbbnzOlJ8E1f+vXrGbNuyu8/4Od3mEOHNtk9/4RWqkZpJvw9mZ93TKHFJ63jbG/Sozn/2/IrP2ycyLD5o8maI5vV9jTp0jDlwDS6je4ZZ9+kqnbdauw/spGDx7z59PMP42x3cXHmrxkTOXjMm01/LyFX7hwx24oWK8SGLYvYfWAdO/etIVUqFwBWrZvD/iMb2bZ7Fdt2ryJr1iyJ1h6RcKRjJF5LBoOBr8YNoM+7A2hTvRMNW9Ul31t5rGIC/YMZ0e9bNq7wjvcYvb7syZF9PomQbcIzGAx8MfZz+nUaRPuaXajfog55C75pFRPkH8yoz8ayacWWOPs/jHhIp3rd6VSvOwO6Dk6stBNVy8b1+H38GEenYTcGg4FeY3ox/P3hfFznY2o0r0GugrmsYi75XqJfk370btCb3et288GQD2K2LftjGT99/lNip/1KDAYDY34YxnvtP6HW281p0aYxBQvls4p5p3Nr7oTfpWq5xvw5ZQ5DRpq/+Gjaoj4uqVyoW7U1jWq1p3PXduTM5UmhIgXo2KUNTet2pH61NtStX4O8+XI7onkvTRkU74zuzqSuYxld73PKN6+Ce4EcVjHXT/sxrtlXfNtoED4b9tNqsLnjl6/MW+QvV4gxDQfyTf0BvFkqPwUrFXVEM16KwWBg/ITRtGrZlbJl6tGuXXMKFy5gFfN+1/aEh9+hZImaTPp1Gt+M+cpq+/c/fM3mzdut1s2ds5SWLd+3d/oJThkMfPDNR4x7fzT96/alSvNq5Cho3RH0873M4KYD+KLhZxxYv5dOg63b2X7Au5w+4JuYab8Sg8HA9/8bQYc2PalSvjGt2zblrUL5rWI6dWlHePgdKnjV4/fJMxkxahAARqORKX/+yMDPRlC1YhNaNHmPyMiomP0+7jGQWlVbUKtqC0JDwxK1XYkuWtv/xwGkY2QnSqk8SqlT8azfrpQqlwDH76qUmvSqx3ldFS9dhOtXbuB/LYCoyCg2rdxKzQbVrGICrwdx4cwlouP55SxSshCu2bKwb8ehxEo5QRUrXYTrfv74XwskKjIK71VbqdGgqlVM4I0gLp65jE6hN1i+SDmvEmTMkN7RadjNW15vEeAXQNC1IKIio9i5Zidv13/bKubEvhM8jHgIwFmfs2T1eFItOL7nOA/uPUjUnF+VV9kS+F25xrWrN4iMjGLV8g3Ub1TbKqZ+49osWbgKgHWrNlO1ekUAtNakTZsGo9FI6tSpiHwUyb1/7lHgrXz4HD5BxIMITCYT+/cepmGTOonetv8ij1cBQq4GEXr9JqZIE4fX7KVU/fJWMef3+RIZ8QiAyz4XyOxu/hZco3FO5YKTsxNOLs4YnYz8E3In0dvwssqV8+Lypav4+V0nMjKSpUvX0LRpfauYpk3qM2/uMgBWrFhPzZqVn2xrVh+/K9c4c+aC1T579hwkLCzpt/9pBbwKEuwXyM3rwZgio9i7Zjfl61W0ivHdd4pHlmvggs85XD1cY7blLZ6fTFkzcWLnsUTN+1WUKVeSK5evctVyDaxYto5GTepaxTRqUoeFC1YAsHrlRqrVNL821qpTldO+5/A9dRaA22HhRKfQIWWvK+kYvcaUUol2j5lSyphYz2WL7B7ZCA64GbMcHHiTbB7ZnrPHE0op+o/sw4TRk+2Vnt1lc8/6VPtDbG4/gEsqF2ZtmMr0NVOo0bDqi3cQSY6ruyuhAaExy6GBobi6uT4zvkGHBhzedjgxUrMbD4/sBPoHxSwHBQTj4ZHdKsY9VozJZOLu3XtkzpKJdau9uX//AUfPbOPgCW/+mDyT8PC7nDtzkYpvlyVT5oykTpOa2vWq4ZnDPVHb9V9lcsvC7YBbMcu3A2+Rye3Zw3+qtK+N73bzB+ArRy9wbp8v3x2ayvcHp3J653GCLvnbPedX5enpxg3/gJhlf/9APDzdnhljvgb+wdU1M2nTpqF//48ZO/bnRM3ZnrK4Z+FW4JPXgVuBt2I6v/Gp1aEux7YfBczvhe8N68bcsbPsnmdC8vBwI+DGk9eBgICgONeAh4cb/jcCgSfXQJYsmclfIA9aw+IV0/h75wr69uthtd8vv41j2+5VDPiil/0b4mg62v4/DiCTL9iXk1JqFlAaOA90ib1RKdURGAIoYJ3W+ssXrO8GDAYCLcd7+KwnVkrNBCKAYoAb0F9rvVYp1RVoAqQG3gBqK6UGAe2BVMAKrfUIpdQbwGIgJ2AEvtFaL1JKfQc0B6KAzVrrgZbnWqu1Xmp57nta63RKqZrACEu+XkBRpVRn4FPABTgA9NJam172xL4ypeKu07ZVRtp3a83urfusOhbJjYqn/drG9gM0K9+O0OBb5MjtwW9LJnLxzGX8rwa8eEeRZLzMNVCrVS0KlizIF+2/sHda9mVDm591XrzKliDaZKJs0dpkzJSB5etmsWv7fi6ev8xvv0xnwfI/+fff+5w+dZ4oU+K/pP0XL3MNVGhZjTdL5mN8h5EAZHvTDfcCORhSyXwP3qdzv6ZAhSJcPHjGbvkmBJva/IyYYcM+Z9Kv0/j33/v2Si/RKeJ7L4w/tmqrGuQvUYCRHYYCUL9LI45tO2LVsUoObLkG4o1B42Q0UrFSGerVbMuDBw9YvmYWx475smvHPj7qMZCgwGDSpXuDGXN/pX3HlixesNJu7RD2IR0j+yoEdNda71FKTQdivkJQSnkC3wNlgdvAZqVUS+DgM9YfAEZZ1t8BtgEvusElD1ADyA9sU0o9Hkj9NlBSax2mlKoPFAQqYO6IrVZKVQeyAQFa6yaWfDMqpbIArYDCWmutlMpkwzmoABTXWl9RShUBOgBVtNaRSqnfgE7A7Ng7KKU+BD4EyJk+H1nTJvy3rzcDbuLm+eSbYjeP7IQE2fbiXrJscUpXLEn7rq1JkzYNzi7OPPj3Pr98+3uC52kvNwNDnmp/NkJtbD9AaLD5W2b/a4Ec3XuMQsULSscomQkNDCWr55OhcVk9shJ2M+6YeK+qXnTo04Ev239J1KOoONuTk8CAYDxiVXPcPd0ICgqJNyYwIBij0UiGDOkIv32Hlm0as33rHqKiorgVGsahg8coWboY167eYOHc5SycuxyAL4f1IzAgiOTgdtAtMns+qRJm9nDlzs3bceIKVylBwz6tmNBhZMw14NWgAld8LvDwvvn7Od/tPuQtXTDJd4z8/YPImcMzZjlHDg+CAq2/5AqwxAT4B1mugfSEhYVTrrwXLVs1Zsy3g8mYMQPR0dFEPHzIH7/Pfvppko1bQbdwjTVE1tXDldvBcV8HSlQpSes+bRnZfljMNfBWmUIULl+Ueu81IvUbqXFydiLi3wgWfD8n0fL/LwICgvDM+eR1wNPTPe41EBBEjpwesV4H0nM7LJyAgGD27jlEWJj592TL5h2UKlWUXTv2ERQYDMC9e/+ybPEaypQtmbI7Ril0mL0MpbOv61rrPZbHc4HYY47KA9u11iFa6yhgHlD9Oesrxlr/CFhkw/Mv1lpHa60vAJeBwpb13lrrx6989S0/PsBRS0xB4CRQVyn1vVKqmtb6DnAXcxXqL6VUa8CWr80Oaq2vWB7XwdyxO6SUOmZZzvf0DlrrqVrrclrrcvboFAH4HjtL7nw58cztgZOzEw1a1mH75t027Tu09ygal2tDk/JtmTB6MmuXbExWnSKA08fOkjtvTjxzmdtfr0Uddm7e8+IdgfQZ0+Hs4gxAxiwZKVm+BFfO+9kxW2EP54+fxzOvJ2653HBydqJ6s+rs995vFZOvWD76juvL6O6juXMr+d0/8bTjR0+RN19ucuXOgbOzEy1aN8J74zarGO8N22j3jnl2tiYt6rNn1wEAAm4EUrl6BQDSpE1DmXIluXTe/NLmapl9yjOHO42a1mHVsg2J1aRXcvX4JbLn8cA1ZzaMzkbKNavMCW/r4ZI5i+Xh3bE9mdLjB/65dTdmfVhAKG9VLILBaMDgZKRgxaIEXUz6Q+mOHDlO/gJ5ePPNnDg7O9O2bTPWrbOeYGfdem86dW4DQKtWjdmxYy8A9eu1p2iRqhQtUpXJk6fz04+Tk3WnCODS8Qu45/UgW67sGJ2dqNysKoe9D1rF5CmWlx7jevFD97HcjfU68Gu/CfSu3JO+VT9k7rcz2bl8W5LvFAH4HDlJvnx5yG25Blq1acLG9VutYjau/5t3OrYCoHnLhuzasQ+Av7fuolixQqRJkxqj0UjlKhU4d+4SRqORLFkyA+YZLOs3rMXZ0+cTt2EiQUjFyL6e7k7HXo6nfv3c9fEd778+/79PPd84rfUfcRJRqizQGBinlNqstR6tlKqAuUPzDtAHqI15WJ3Bso/CPEzusaefa5bW2uHTmJlMJr4fMoHfFozHYDSyasFaLp+7widf9OD0sbPs2Lybol6FGT99HBkypad6vSp8PKgHbWskr6l4n8VkMvHD0In8Mv8njEYDqxeu5/J5Pz4a9AFnjp9j5+Y9FC1VmB+mjSFDpvRUrVeZjwZ+QIda75O3YB4Gfz+Q6OhoDAYDsybP48qFq45uUoIbNOI7DvmcIDz8LnVadqZX9/do06yBo9NKMNGmaKZ8PYUxc8ZgMBrYvGgz185fo3P/zlw4eYED3gfoPrQ7qdOmZvAU869sSEAIo7uPBuCHpT+QK38uUr+RmtkHZjNx0ESO7jzqyCa9kMlk4usvxjJv6R8YjEYWzVvB+bOXGDi4N8d9fPHeuJ2Fc5fz8+/j2H14PeG379Crh3k2qpnTFjB+0hi27l2JUorF81dyxvLBZ+qsCWTOkomoyCiGfvEtd+7cfV4aSUa0KZqFw6fTd/ZQDEYDexdvI/DCDZp+3p5rJy9xYssR2gzuTKq0qen5m3l2vtv+oUzp+QNH1++nUOXiDNv0E2jw3XGMk1uPOLhFL2YymRjQfzirVs/GaDQye/Zizpy5wLCvP+fo0ZOsX7eFWTMX89e08Zw4uZ3bt8N5v0vfFx535sxfqFa9Eq6umTl/YR9jxkxg9qzFidCiVxNtimb68D8ZMnsEBqOR7Yu3cOPCddr178jlExc58n/27js8iupt4/j3bBKa9JpCb0oPRaRKL9K7DUXFjsqrgoqAIiLY208EsTdA6dKbFBFQMBB6b5JGDUiTZHPeP3YJ2RSMkuyGcH+uKxfMzDOzz9lsdvbMc+bsknX0ffE+cuXJxdMfu4bSHos8ylsPjvZx5v+d0+nkhcEjmTLjcxx+fkz8dio7d+zhhaFPsTFsCwvm/8z330zh4wlv8fvGxcSePMVD9z8NwKnY04wb+yWLl0/DWsuSRStYvHA5efLkZsqMz/EP8MfPz48Vy1fzzVdZ//d/NWw2nXTC/Jv7CiT9jDFlgf1AI2vtGmPMp8AOoDMwCIgA1nJ5yNxC4H+4htJdaX0dXN8SlE8AACAASURBVJWbn4Fwa+0TaTz+V0BxoBNQDlgBVMTVoal3aT/3ULpXgVbW2jPGmBAgDlen+YS19oJ7KN99QF8gj7X2iHtY3R5rbWFjzDAgn7X2eXfsDNdIO9McGGSt7eR+rKrALFxD6S4dI5+1Ns1P1bUDG1/XL1B/R5aas8LrVm/6ytcpZAld66T6Z35d2HTmkK9T8LnO+bP+NNiZ6Zsjv/9zUDbXqVior1PwqaUnt/k6BZ87dnrXlS6ce92ZIT0z/fNZ3jHTvN5mVYwy13agnzHmE2A3MA5XxwhrbZQxZgiue4UMMM9aOwvgCutHAGtwTWYQhmtShCvZiatDVAJ41N3J8Qiw1i5y3/uzxr3tDK4OUEXgLWNMAq6O0mNAPmCWMSaXO7en3Yf51L3+d2ApnlWipI+1zd2JWmSMcbiPOwDIfuUGERERkewqm95jpI5RJrHWHgBSu8zXPEnMRGBiKvumtf5L4Mt/kcav1tqnk66w1n4FfJVs3QdA8vlH9+KqViVXP5W8YoAGSVYNca9fDixPFvsD6bs/SkRERETEa9QxEhERERGR9FPFSLIiY8xQoHey1VOstff5IB0RERERkWuSOkbXOGvta8Brvs5DRERERK4TNnvOSqfvMRIRERERkeueKkYiIiIiIpJ+2fQeI1WMRERERETkuqeKkYiIiIiIpJvNphUjdYxERERERCT9smnHSEPpRERERETkuqeKkYiIiIiIpF+CpusWERERERHJllQxEhERERGR9NM9RiIiIiIiItmTKkYiIiIiIpJ+qhiJiIiIiIhkT6oYiYiIiIhIulmripGIiIiIiEi2pIqRiIiIiIikn+4xEhERERERyZ5UMRIRERERkfRTxUhERERERCR7UsVIsrToCyd9nYJPXYi/6OsUfKprnSeYFfaRr9Pwuev9OZhdfZivU/Cpo/HX9zXMHYUr+ToFn9tw7rCvU/ApP3N9/w1kRTabVozUMRKRLK1rnSd8nYJPXe+dIhEREW9Rx0hERERERNIvm1aMVJsUEREREZHrnipGIiIiIiKSfgm+TiBzqGIkIiIiIiLXPVWMREREREQk3bLrrHSqGImIiIiIyHVPFSMREREREUm/bFoxUsdIRERERETST5MviIiIiIiIZE+qGImIiIiISLpp8gUREREREZFsShUjERERERFJP91jJCIiIiIikj2pYiQiIiIiIumme4xERERERESyKVWMREREREQk/XSPkYiIiIiISPakipGIiIiIiKSbVcVIREREREQke1LFSERERERE0k8VI5FrX4tWTfhl3VxWhy3gif97MMX2HDkCGP/FO6wOW8DcJZMpWToYAH9/fz4YN5qff53Jyt9m8+TTDwEQHBLI1NlfsvK32Sxf8xMPPtrXq+35t1q1vpXfwxbxR/hS/u+ZR1Jsz5EjB59//QF/hC9l8bKplCodAkCp0iFEHt3CytU/sXL1T7z7wUgA8ua9IXHdytU/sefg74x+Y6hX23Q16jary4RlE/hs5Wf0frx3iu3dH+zO+KXjGbtwLKMnjaZ4SPHEbSO/GcmPm39kxJcjvJix9wwb/S63dryDbn0f9XUqmapEi5q0WfU2bde8S+UnOqcZF9ypPj2iJ1KwVjmP9blDitBl7xdUeqxjZqeaKUo1r8ntK97ijlXvEDogZfur9G1JryVj6LnwNbpMH07BSq73REeAH83feZheS8bQa9FrBDWs4u3UM8zNzevx9Yov+G7VV9w54PYU22veUoNP5n/MkgMLuLVjU49tjwx9kC+XfspXyz7nyZGPeyvlDNW0ZUMWrJnG4t9n8PBT/VJsr9ewNjOWfse2qLW069zKY9tnP3zI+j3L+OT797yVboZo0aoJv66fz9oNCxPP50nlyBHAhC/fZe2Ghcxf+kPiubBn704s/WVG4k/UyW1Uq3ETANPnfMOv6+cnbitatLBX2yQZQxUjuW44HA5Gvz2M27s9SFRkDPOX/cCi+cvYtXNvYsyd9/TkVOxpGtVpT9cetzFsxLM8+sCzdO7Wjhw5ctCycTdy587Fit9mM2PaXC7+fZFXhr3J5vDt3JA3DwuXT2XlsjUex8wqHA4Hb707gu5d+hEZEc3PK6czf95Sdu7YkxhzT7/enIo9Rd1arejRqyMjXn2O/v0GAnBg/yFubdTF45hnzpz1WLfsl5nM+WmRdxp0lRwOB4+Pepyhdw/lWNQx3p/9PmsXr+XP3X8mxuzdupeBHQfy94W/6dC3Aw+8+ACvD3gdgGmfTCNn7px0uLuDr5qQqbp1aMNdPbvw4qtv+zqVzOMw1BpzP6v6jOF81HFaLBhF1KIw/toV4RHmf0MuKvZvx4k/dqc4RM1X7iH653BvZZyhjMPQeFQ/5t71OmejTtBj7kgOLPqD2N2RiTF7Zq5h+3c/A1CmTR0avdyXeX3fpMpdLQCY2noIuYrkp8O3g5ne8SWw19Z3mzgcDgaOepLBdz3P0ahjjJ/7EasXreHg7kOJMTERR3jjmbe4/RHPiyfV6laler3q9G/jusj04Yz3qNWwJuFrNnm1DVfD4XDw8uvPc3/vAURHxjBt0TcsXbCSvbv2J8ZEHY7mhSdH0P/xe1Ls//lH35Irdy7u6NfDm2lfFYfDwevvvESfbg8QGRHDwmVTWDjvZ4/z9l339iI29jQNarejW88ODH/lWR6+/xmmTZnDtClzAKhStTJfTxrL1s07Evd7/KHBhG/Y4vU2+YLuMfoPjDFFjDEb3T/RxpiIJMs5ksUuNMbky8x8/g1jzCpjTGgq6/9TnsaYqsaYcGPMBmNM2TRi/I0xsWls+84Y0+0Kx3/GGJMrHccZYIy5+wrHaW2MmXmltlyratetwYF9hzh08DBxcXHMmjafdh1aesS079CSHye5mj9n1iKaNmsAgLWWPDfkxs/Pj1y5cnLxYhxnTp/lSMwxNodvB+DsmXPs3rWPwKDiZEV169Vi376DHDzwJ3FxcUyfOpcOHVt7xNzWsTWTvp8BwKwZC2jWvGG6j1++QhmKFSvC6l/XZWjemaVyaGUiD0QSfSia+Lh4Vs5eScO2nu3dtGYTf1/4G4AdG3ZQNKho4rbwX8M5f+a8V3P2pnqhNSiQP8u8JWeKwrUrcnZ/DOcOHcHGOTk8cw1B7eqmiKv6fG92fTwH599xHuuD2tfj7KEj/LXzsLdSzlDFQytw+kAMfx06SkKckz2z1lK2rWf745K8xv3z5MS6Oz6FKoUQ8etWAC4cP83F0+colqyadi24KfRGIg9EEuV+H/h51nIat23kERNzOIZ92/eTkOwLLa215MgZgH8OfwJyBODv78/Jo6meerOsmnWqcfDAn/x5MIK4uHjmzlxE69uaecRE/BnFzm17SEjlk/CaX9Zx9sw5b6WbIerUrcn+fYc4eMD1WWDm9Hm07+hZCWvfoRU/TnR9Fpg9cyFNmqU8F3bv1ZEZU+d6JWfxnkztGFlrj1trQ621ocB44L1Ly9baiwDGxWGtbWet/Ssz88kIV5FnD2Cqtba2tfZABqcF8AyQ65+CrLVjrbXfZ8LjZ3mBQSWIiIhOXI6KjE7RiQkMKkGkO8bpdHL69F8ULlyQObMWce7secJ3rmD9lqWM/9+XxMae8ti3ZOlgatSoQtgfWfNqYVBwCSIORyUuR0ZEExRcwiMmOEmM0+nk9KkzFC5SCIDSZUqy4tefmLNgIg0b1Utx/J69OzN92rVzkigSWIRjkccSl49FHaNIiSJpxre7vR3rl633RmriJbmCCnE+8nji8vmoE+QO8hz+UqB6GXIHFyF68QaP9X55clL5ic5sf3uaV3LNDHmCCnEm6kTi8tnoE9wQVChFXLV+rblj1Ts0GHoHv770DQDHtx+iTNs6GD8H+UoVo2iNsuQNTvvvJ6sqGlSUI1FHE5ePRh/zuAByJdvCtrNhdTjT/viBqWE/sG7Feg7tOfTPO2YhJYKKEx0Rk7gcHXmEEln04l5GCQwuQWSE57kwMMjzXBgUVJyIiMvnwr/cnwWS6trjthQdow/GjmbpLzN4evBjmZR9FpLghR8f8Mk9RsaYisaYLcaY8UAYEGSMOWyMKejefr8xZpO7wvKle10JY8x0Y8x6Y8zvxpgG7vWjjDFfG2OWGWN2G2MecK8PcVd9Nrofq1EaufgbY741xmx2xz2VbLufu1ozwr182BhTMEkbPjfGbDXGzL9UsUnlMboATwCPGmOWuNc9595/izHmyVT2cRhjPjbGbDPGzAbSfKc2xjwNFAd+uXR89/rX3c/hGmNM8STP1/+5/1/ZGPOzOyYseSXLGHPLpfXu/T43xqwwxuwzxgxIEtfP/TvZ6M7Zkdbzaox52t2mcGPMd2m1KTMYY1KsSz7oI9UYa6ldtwYJzgRCb2pO/VpteeSJ+yhdpmRiTJ4b8vD5Nx/w0otjOPPX2YxOPUOk1bZkQanGxEQfpUaVW2nWuAtDX3iNT794j3z58nrE9ejViWlTZmdozpkpXc+HW4vuLahUsxJTP5ma2WmJF6X2GvAYCmYMNUfew+ZXUr5VVRnckz0T5uE893cmZpi5DKm1P+WqrV8vYXKTZ/lt9GTqPOUauLBj8grX8Lt5r9JoRF9i/thNQrwzkzPOeKk9B2m9DyQXXDaYMpVK0/vmO+ld7w5qNw6l5i01MjrFTJX6n8C1NRzy30qtzSmGgKZ6frj8/zp1a3L+3AV2bL88vPbxhwbRvFEXutzWlwaN6tH7jq4ZlLF4ky/vMaoK3G+tfRQun6CMMbWA54FG1toTxphLl+8+BN601q51f4CfA1R3b6sBNALyA2HGmLlAX2C2tfYNY4wfkDuNPOoCRa21NdyPn/SSgD8wEQiz1r6Ryr43AndaazcbY6YD3YDJyYOstT8ZY+oDx6y177v/fzdQH/ADfjfGrAC2JdmtF1DO3cZg97bxqTXAWvueMeZZoKm1NtYY4w8UAFZYa18wxrwLPAC8nmzXScAIa+1sd6fOAVR0Pw9NgfeALtbaw+7fT2WgFVAQ2O7u2FYBuuP6fcUbYyYAdwB703henwPKWGsvJnuuExljHgYeBsifO5A8OVJewfwvoiKjCQkJTFwOCg4kJupIipjgkECiImPw8/Mjf/58nDx5iu69OrJs6S/Ex8dz/NgJ1v22gVq1q3Po4GH8/f35/Jv3mT5lDvNmL0n+sFlGZEQ0ISWDEpeDQwKJTtb+SzGRkdGu9hfIy8kTrqEhF09cBCB841b27z9EhYpl2egeS129+k34+/kRvnGrl1pz9Y5FHaNo8OXrDUWDinLiyIkUcaFNQrn9idt5vs/zxF+M92aKksnOR54gd5IqR+6gwpyPPpm47J83F/lvLEXT6cMByFWsAA2/HsSafm9TuHZFQjrdQvXhdxGQPw8kWJx/x7Hvi2vjHjuAs1EnyJukQnZDYGHOJml/cntmraXJ6PsBsM4E1rxyefBB15kvcWp/dFq7ZllHo45SPKhY4nKxwKIcjz5+hT0ua9q+MdvCtnPh3AUAfl+2jqp1qrDpt82ZkmtmiI48QmDI5WpJYHBxjkQfvcIe176oiBiCQ5KdC6OTfxaIISQkKPGzQL78+Th58vIwyW49OzAj2QiJS+fTs2fOMn3KHGrXrcmUybMysSW+pXuMMt5ea21qNyO0BH6w1p4AuPQv0BoYb4zZCMwEChljLnV2ZlprL1hrjwArgZuBdcCDxpiXgerW2jNp5LEHuNEY84Exph2QdHzU56TdKQLYY6299A74B1D2H9p8SVNgmrX2nHtY3kygSbKYW4FJ1toEa+1hYHk6j33JeWvt/LRyM8YUwtVxmQ3gfv4uDRSuDnwMdHI/9iVzrLUX3c/zCaAYrt/LzcB69++mGVCBtJ/XrcB3xnWfk+eAfTdr7QRrbT1rbb2M6hQBbAzbQrkKZShVJoSAgAC69ryNhfOXecQsnL+MPne6roh26tqWVSt/AyDicBSNb3Xdb5Q7T27q1qvFnt37AHj3o1fZvWsfn4z9OsNyzQxhf2yiQoUylC5TkoCAAHr06sj8eUs9YhbMW8qdd3cHoGv39qxcsRaAIkUL43C43i7KlC1F+QplOHDg8iQFPXt3ZtrUOV5qScbYFb6L4HLBlChVAv8Af27tfCtrF6/1iClfrTxPjnmSkf1Hcur4qTSOJNeqkxv3krd8IHlKF8ME+FGyW0OiFv2RuD3+r/PMrfYIC28eyMKbB3IibA9r+r1NbPh+VnYbmbh+76cL2PnhrGuqUwRwJHwfBcoFkq9UMRwBflTs2oCDi8M8YvKXu/yhuUyrUE67Oz/+uXLgnzsnACFNq2PjEzwmbbhW7AjfSUi5EAJLBeIf4E/Lrs1ZvXhNuvY9EnGEWg1q4vBz4OfvR60GNT0mbbgWbN6wjbLlSlGydDABAf507NaWpQtW+jqtTLUhbDPlK5ShtPuzQLceHVg472ePmIXzfqbPXa7PAp27tWPVysvnBmMMnbu1Z2aSjpGfn1/iUDt/f3/atG/Oju27vNAayWi+rBilNd7IkGoxHwPUv3RvUuJKVyUjeby11v5sjGkOdAS+N8aMSe3eGmvtcWNMTeA24CmgJ+5qBfAr0MoY8761NrXxEknXOUn/85laITc1V1PPTvo8pZVbWsePBG4AQoEFSdan1l4DfGGtHZ78IGk8r+1wdZ66AsOMMdWttV4Zf+F0Onlx8GtMmvYpfn4OJn83g1079jD4xScI37CVRfOXMenbafzvkzdYHbaA2JOxPPrAIAC+/GwS7499jeVrfsIYw+TvZ7B96y7qN6hD7zu6sm3rThb/Mh2AMSPf5+fFWe/E4nQ6ee7ZV5g280v8/Pz4/tsp7Ni+myHDBrIxbAvz5y3l269/ZPxn7/BH+FJOnoyl/33/B0CjxjczZNj/4YyPx+lM4NmBLxF78nJHoVuP2+jTM+X051lZgjOBccPHMerbUTj8HCz6YRGHdh2i7zN92b15N78t/o3+Q/uTK08uhowbAsDRyKOM7O+aqvzNqW9SqkIpct2Qi29++4b3B79P2MqwKz3kNWXwy6+zbsMmYmNP06pbXx7vfw89O7fzdVoZyjoT2PjiVzSe9ALGz8HBScv5a2cEVZ7rRezGfUQtyj6/z9RYZwKrhn9Nh++fwzgc7PxhBSd3RVBvUE+Ohu/n4OIwqt/XlpAm1UiId/L3qbMse/oTAHIVzU/H75/HJiRwNvokPw8c5+PW/DcJzgQ+HP4Rb34/BofDwfwfFnJg10HuH9SPneG7WL14DTfWqsyrn40gb4G8NGzTgPufuZf7Wz3Eirm/ULtxKF8s+RRrLeuWr2PNkrX//KBZiNPpZOSQt/j8x//h5/Bj6qSf2LNzH089/whbNm7n54UrqRFalbFfv0X+Avlp0bYpTz33MB2buqY1nzj7U8pXLEueG3KzMnwuL/7fq6xalrWfA6fTyZBBrzJ5+uf4+TmY9N00du7Yw3MvPkn4hi0snL+Mid9O5aMJb7J2w0JiT57ikQeeSdy/YeObiYqM5uCBy9eNc+bMweQZnxPg74/Dz8Evy9fw3VdTfNE8r8muFSPjrbGk7nt0zlhr3zbGVMQ1EUFoku2HcVUqSgM/kmQonfvfH4E11tr33PGh1tqNxphRuD58NwLyARuAergmIjhsrXUaYwYBgdbaQankVQy4YK39yxhTDxhvra1njFmF676gtkBDoLd7qNilPIsmbYMx5gXA31o7Ko32j8JzKN0n7pz9gN+B24Ht7piCxpg+QD+gMxCEayhdP2ttqjPGGWO2A22ttX+6h9Ids9ZeumfrDqC1tfbBZHmsB15JNpSukbvdjwELgQHW2l+S7uc+5g5c1aJCwFSgsbX2mDGmCK5O1fnkzytwC1DSWnvQuGYljATKXWkyi6CCVbP3YOd/cCH+4j8HZWMNC1f2dQo+NyvsI1+n4HOzqw/zdQo+ddT/+v7KwcnE/HNQNhfxd9pDHK8Hpy6mNejn+hFzakd6L6p7RUyLZpn++azEshVeb3OW+x4ja+0mY8ybwEpjTDyuYWD9gQHAOGPM/bjyXuZeB65hc/OBUsDL1toY45qE4RljTBxwBtc9R6kpBXxuXKUni+v+pqT5vGmMeQ34yhhzbwa18XdjzCR33gDj3PcpJf19TAVaAFuAnbiGCF7JBGCJMeZPoH06U7kb+MTdvou4qjqXcowyrkkj5l2p3e68X3E/tgPX8LhHcVWUkj+v/sBE45ru3AG8cS3MRCgiIiIi2Z/XKkaZJXklQ7IXVYxUMbreqWKkipEqRqoYqWKkilGWqxg1b575FaPly/+xzcaY9sAHuEZgfWatTT7RGO5RWCNwXagPt9beldbxslzFSERERERE5Ercs06PBdoAh4F1xpifrLXbksRUAobguuXjpHF/fU1arvmOkbU23ZcS3ffUJG/zXUmfwKvlnsK6QbLV71prv8mg4/+E6z6spAZZa7PuPNEiIiIikm1kkckX6uOaIXofgDFmMq7JvZJ+rn8IGGutPQngnlk5Tdd8x+jfsNbW88JjPJrJx++SmccXEREREbkGhAB/Jlk+jGuir6QqAxhjfsU13G6EtXYBabiuOkYiIiIiInJ1bELm3/JkjHmYy1+hAzDBWjshaUgquyW/98kfqAQ0B0oCv7i/KiY2+Y6XgkVERERERLIMdydowhVCDuOaXfqSkri+CiZ5zFprbRyw3xizE1dHaR2puL6nuhERERERkX/FJmT+TzqsAyoZY8q5vx/zDuCnZDEzcX39DcaYoriG1u1L64DqGImIiIiIyDXFWhsPPAEsBLYDP1prtxpjRrq/ixP3tuPGmG24vgN1sLX2eFrH1FA6ERERERFJN2uzxtcqWWvnAfOSrXspyf8t8Iz75x+pYiQiIiIiItc9VYxERERERCTdssj3GGU4VYxEREREROS6p4qRiIiIiIikmze+x8gXVDESEREREZHrnipGIiIiIiKSbtb6OoPMoYqRiIiIiIhc91QxEhERERGRdNM9RiIiIiIiItmUKkYiIiIiIpJu2bVipI6RiIiIiIikmyZfEBERERERyaZUMRIRERERkXTTUDoRH0iwCb5OwaeeLHqLr1PwqS9Oh/s6BckCOm8Z5esUfK5j7cd9nYLvZNMhO/9GxNljvk7Bp8rlC/R1CnKdUMdIRCQLm119mK9T8Cl1ikREsh5rs2fFSPcYiYiIiIjIdU8VIxERERERSbfseqeDKkYiIiIiInLdU8VIRERERETSLUH3GImIiIiIiGRPqhiJiIiIiEi6aVY6ERERERGRbEoVIxERERERSTeboIqRiIiIiIhItqSKkYiIiIiIpJu1vs4gc6hiJCIiIiIi1z1VjEREREREJN10j5GIiIiIiEg2pYqRiIiIiIikW4K+x0hERERERCR7UsVIRERERETSzWbTipE6RiIiIiIikm6arltERERERCSbUsVIRERERETSTZMviIiIiIiIZFOqGImIiIiISLpl18kXVDGS60qLVk34df181m5YyJNPP5Rie44cAUz48l3WbljI/KU/UKp0CAA9e3di6S8zEn+iTm6jWo2bPPb9ZtLHrFjzk1fakREqNqvJU0vfYuDyd2j6WOcU2+vd3YoBC17nsXmj6T/lJYpVdD0XIbXK89i80Tw2bzSPzx9NlXb1vJ36f9a8VWNW/DabVevnMWBg/xTbc+QI4OPP32bV+nnMXjyRkqWCAfD39+e9sa+xZNV0lq39iQH/92DiPv0f6cuSX2ewdPVM+j/a12ttyQglWtSkzaq3abvmXSo/kfI1cElwp/r0iJ5IwVrlPNbnDilCl71fUOmxjpmdqk8MG/0ut3a8g259H/V1Kl5Rr3ldPl/+GV/+8gW3P94nxfaeD/Xg06WfMH7RON6YNIbiIcV9kGXGurl5Pb5e8QXfrfqKOwfcnmJ7zVtq8Mn8j1lyYAG3dmzqse3hFx/kiyUT+GLJBFp0buatlDNEmzbN2LBxKZs2L+fZZx9LsT1Hjhx8/c1HbNq8nOUrZlK6dEmP7SVLBhNzZCsDB7rOoyEhQcybP4k/wpawbv0iHn/8fq+0IyM0btGA2b/+wLy1U+j/5D0pttdtEMqPi79mY8Qq2nRqkbg+qGQgPyz6iqlLv2Hmion0ube7N9OWTKKOkVw3HA4Hr7/zEnf1eoim9TvRvWdHKt9YwSPmrnt7ERt7mga12/HJx18z/JVnAZg2ZQ6tmnanVdPuPPHI8/x5KIKtm3ck7tehcxvOnj3n1fZcDeMwdBp5H9/e9yYftXmOGl0aJnZ8Ltk8azVj27/AuA4vsuqTObQffjcAR3Ye5pPOwxjX4UW+ufdNOr/2AA6/rP9W4nA4GPXmMO7p8xgtGnaha88OVLqxvEfMHX17cCr2NE3qdeDTcd/y4ohnAOjUtS05cuagdZMe3NaiD33v603JUsHcWKUid97bk06t76Rt0560btuMcuVL+6J5/57DUGvM/fx615ssvnUwJbs3Il/lkBRh/jfkomL/dpz4Y3eKbTVfuYfon8O9ka1PdOvQhvHvjvJ1Gl7hcDh4YtQAht47jIdaPkzzrs0pXcnztbxnyx6e6PgUj7Z9jF/mreLBoSkvLlxLHA4HA0c9yQv3vMh9LR6kVdcWlEnW5piII7zxzFssnfmzx/oGLetTqXpFHmz3KI93forbH+1Dnrx5vJn+f+ZwOHj3vZF073Yfdeu0oXfvLtx0U0WPmH739SE29hQ1azTno/99zqujXvDY/sabw1m0aHnistMZz4tDRlG3TmtaNO/Ow4/ck+KYWZHD4WDY64N47K6n6dL0Tjp0b0v5ymU9YqIiYhg28FXmTV/ksf5ozDH6dnqIXq3u5c7b+tP/yXspVqKoF7P3LWsz/8cXsv6nGclUxphHjTH3ZvAxvzLG9HL//zNjTNWMPP5/VaduTfbvO8TBA4eJi4tj5vR5tO/YyiOmfYdW/DhxJgCzZy6kSbOGKY7TvVdHZkydm7ic54Y8x0OVDAAAIABJREFUPDrgPt57a1zmNiADlQytwImDMZz88yjOOCebZ6/lprZ1PWL+PnM+8f858uQE95tU3IWLJDgTAPDPGZC4PqsLrVuDA/sPcejgYeLi4pk1fT5tb2vpEdO2Q0umTJ4FwNxZi2hy6y0AWGvJkyc3fn5+5MqVk7iLcZz56wwVK5dnw/pNXDh/AafTydrV61O8prKqwrUrcnZ/DOcOHcHGOTk8cw1B7eqmiKv6fG92fTwH599xHuuD2tfj7KEj/LXzsLdS9rp6oTUokD+fr9PwihtDbyTyQBTRh6KJj4tnxU8raNTW8/0vfM0m/r7wNwDbw3ZQLPDa/hB4U+iNRB6IJMrd5p9nLadx20YeMTGHY9i3fT8JCZ5vdGUqlyF87SYSnAlcOH+Bvdv3Ur/5tVE9r1cvlH17D3LgwJ/ExcUxdepsOnVq6xHTqWNbvv9uGgAzZsyjefPLz0unzm05sP8Q27dfvlgSHX2UjRu3AnDmzFl27txLcHCgF1pzdWrUqcqh/Yc5fDCS+Lh45s9cTMv2t3rERP4Zxa5te1K8BuLj4om76HpfzJEzAIcjew4tu96oY3SNMMZkyv1g1trx1tpvMuPY7uM/aK3dllnH/zcCg0sQGRGVuBwZEU1gUAmPmKCg4kS4Y5xOJ3+d/ovChQt6xHTtcZtHx+iFoU8x7qMvOX/+QiZmn7HylSjMqcjjicuno06Qv0ShFHH172nD/614l7Yv3MncEV8nri8ZWoEnFr3BgIWvM3vYF4kdpawsKKg4URHRicvRkTEEBXkOBQpMEuN0Ojl9+gyFChdk7k+LOXfuPGHbl/H7psV8MvYrYmNPs3P7Hm5pWJeChQqQK3cuWrZpSnBI1v8wAJArqBDnk7wGzkedIHdQYY+YAtXLkDu4CNGLN3is98uTk8pPdGb729O8kqtkvqKBRTgaeTRx+WjUMYoEFkkzvv0d7Vi3fL03Uss0RYOKciQqSZujj1E0KH2dvb3b9nFLi/rkzJWT/IXyE9owlGLB18bQwuDgEhyOiExcjoiIIii4RJoxrvfCvyhSpBB58uTmmWceZfToD9I8funSJalVqyrr1m3MnAZkoOKBxYiOPJK4HBN5hOKBxdK9f2BwcaYv+44lYT/x+UffcjTmWGakmSUlWJPpP76gjpGXGWNuMMbMNcaEG2O2GGNuN8bUNcasMMb8YYxZaIwJcscuN8aMNsasAAYmrcS4t59x/9vcvf+PxphdxpjXjTF3G2N+N8ZsNsZUSCMdjDEjjDGDkjzeG+79dhljmrrXV3Ov22iM2WSMqWSMKWuM2ZLkOIOMMSNSOf5yY0y9S/kaY15zt32tMaZE8vjMZFL7G0teq00lKGlInbo1OX/uAjvcV8qq1biJcuXLMH/OkgzMNPOl9lzYVOrWv3+7mPebPcOi1yfT7MluiesPb9zLR22f55Muw2n6WBdX5SirS/V3a5OFpB4TWrcGCU4ndau2pGHt9jz8eD9KlynJnl37+PjDL5g0/VO+mzKebVt2Ee90ZloTMlJqbfV4sRtDzZH3sPmV71KEVRnckz0T5uE893cmZihelY6/j0tadW9J5ZqVmDJ+amZnlakM6W9zcutX/sHan3/no1kfMHzsi2wL20bCNfy3n6LdacQMG/Y0H/3v8zSHjt9wQx4mThrHc8+N5K+/zmRIvpkp1efiX+wfHXmEHi360qFBL7re3oEixQr/806Spalj5H3tgUhrbS1rbXVgAfA/oJe1ti7wBfBakviC1tpm1tp3/uG4tYCBQA3gHqCytbY+8Bnw5L/Iz9+93/8BL7vXPQp8YK0NBeoB/3XszA3AWmttLWAlkHL2A8AY87AxZr0xZv35i7H/8aFSioqIITgkKHE5OCSQ6OgjnjGRMYS4Y/z8/MiXPx8nT17OoVvPDsyYdrlaVK9+KDVDq7Fu01J+WvA95SuWZfqcTCvAZZjT0ScoEHz5anD+oML8dSTt53rL7DVUaZNymMixvZHEnf+b4pVLprJX1hIVGUNQkmpOYHAJoqOPphnj5+dH/vx5iT15im49O7B86a/Ex8dz/NgJ1v2+kZq1qwEw+bvp3NaiD7063UfsyVPs33vQe426CucjT5A7yWsgd1BhzkefTFz2z5uL/DeWoun04bRb9wGF61Sk4deDKFirHIVrV6T68Ltot+4DKjzUnhuf6kr5B9qm9jByjTgWdYxiwZevlBcLKsqJmBMp4mo3qc2dT97Byw+MSBxGdK06GnWU4kFJ2hxYlOPRx6+wh6fv/zeRh9o9yuC7XsAYw+H9EZmRZoaLiIimZEhw4nJISBDRUZ7nwsgkMa73wnycOBFLvZtDGfXaELZtX8WAAQ8waPAAHnnUNRrf39+fiRPH88Pkmfw0a6H3GnQVYqKOEJik0lciuDhHk50X0uNozDH27NhPnVtqZWR6WZq1JtN/fEEdI+/bDLR2V2aaAqWA6sBiY8xGYBiQ9FPmD+k87jprbZS19m9gL3DpLsHNQNl/kd90979/JNlvDfCiMeZ5oIy19nxqO6bDRWBOKsf3YK2dYK2tZ62tlztHwdRC/pMNYZspX6EMpcuEEBAQQLceHVg4z/OG2oXzfqbPXa7KSOdu7Vi1cm3iNmMMnbu1Z2aSjtHXn0+m1k23cnPNVnRpfzf79hygR6cMvWUrU0SE76Nw2UAKliyGX4AfNTo3YMfiPzxiCpe9XNCr3DKU4wdcQ8wKliyWONlCgZCiFCkfROzhf38i8bbwsC2UK1+aUqVDCAjwp2uP21i8YJlHzOL5y+h9R1cAOnZty6+//AZA5OEoGt1aH4DceXJTp15N9u7aD0CRoq4rhMEhgdzWqRWzps33VpOuysmNe8lbPpA8pYthAvwo2a0hUYsuvwbi/zrP3GqPsPDmgSy8eSAnwvawpt/bxIbvZ2W3kYnr9366gJ0fzmLfF4uu8GiS1e0M30lI2WACS5XAP8CfZl2asWbxWo+YCtUqMPD1J3npgRHEHj/lo0wzzo7wnYSUCyGwVCD+Af607Nqc1YvXpGtfh8NB/oKu+8/KVylH+ZvKsW7FtTG08I8/wqlQsSxlypQkICCAXr06M3fuYo+YufMWc3ffngB0796BFStWA9C2TR+qVmlC1SpNGDv2C95+ayyfjHddDBw37g127tzD//73uXcbdBW2bNhO6fKlCCkdhH+AP7d1a8Oyhb+ka98SQcXImSsnAPkL5KN2/Zoc2HsoM9MVL9D3GHmZtXaXMaYu0AEYAywGtlprU97l73I2yf/jcXdmjav+myPJtqRjWhKSLCfw737Pl/ZzXtrPWjvRGPMb0BFYaIx5ENiFZ8c6VzqOHWcv1+sTj+8tTqeTIYNeZfL0z/HzczDpu2ns3LGH5158kvANW1g4fxkTv53KRxPeZO2GhcSePMUjDzyTuH/DxjcTFRnNwQPX/s3mCc4E5r70Ffd+8zwOPwdhP67g6O4IWj7dk4jN+9m5JIxb+rWlQuPqOOOdXDh1lunPjgegzM030vSxzjjjndiEBOYM/5JzJ7P+kAmn08nw50bz/dRPcPj58cP3M9i1Yy+DhgwgfMNWFi9YzuTvpvPB+DGsWj+P2JOnePzBwQB89fkk3v1oFEtXz8QYw48TZ7J92y4AJnz9HoUKFyQ+Lp6hz73GqVOnfdnMdLPOBDa++BWNJ72A8XNwcNJy/toZQZXnehG7cR9Ri8J8naLPDX75ddZt2ERs7GladevL4/3voWfndr5OK1MkOBP4aPjHjP7uNRx+Dhb+sIiDuw5y77P3sGvTbtYuXstDQx8kd57cDB8/FIAjkUd5+YERvk38KiQ4E/hw+Ee8+f0YHA4H839YyIFdB7l/UD92hu9i9eI13FirMq9+NoK8BfLSsE0D7n/mXu5v9RB+AX58MP09AM6dOcdrT71xTdxrCa73wmefeYlZP32Dn58f33zzI9u372bY8KcJC9vMvLlL+PqrH/ns83fZtHk5J0/G0u/eKw88adiwHnfd3ZMtm7ezZu08AEa8/CYLFy73Qov+O6fTyeghb/PJ5A/w83MwY9Ic9u7cz4DnHmJr+A6WL/yF6qFVeP/LN8hfMB/N2zZhwOCH6NbsLspXKsfgV57CWosxhq/Gfc/u7Xt93SSv8dU9QJnNpHc8rWQMY0wwcMJae8EY0w14GKgM3GOtXWOMCcA1DG6rMWY5MMhau9697zAgn7X2efe+M6y1xhjT3B3XyR2XuF/ybankMwI4Y619O9l+RYH11tqyxpjywH7rerD3gQPAWCAKuBE4A6wAFlhrRxhjvgLmWGunJjvmGWttXvfj9gI6WWvvu9LzVaLATdf1C/SRQilnCbuefHE6+04FnV4f5qjp6xR8qvOW62O67H/Ssfbjvk7BZ+LstXHvTmb6/UTK6fKvJ+XyXRuT2mSmLTFrs1RP5LfgHpn++eyWyOleb7MqRt5XA3jLGJMAxAGP4aoEfWiMKYDrd/I+sDWVfT8FZhljfgeW4llNyky3A32NMXFANDDSWhtnjBkJ/AbsB3Zc6QAiIiIikj1k16vWqhhJlqaKkSpG1ztVjFQxAlWMrneqGKlilNUqRmu9UDFqoIqRiIiIiIhkZdn1HiN1jK4TxpihQO9kq6dYa19LLV5ERERE5HqijtF1wt0BUidIRERERK6Kr75nKLPpe4xEREREROS6p4qRiIiIiIik27XxrV3/nipGIiIiIiJy3VPFSERERERE0s2SPe8xUsdIRERERETSLSGbfsukhtKJiIiIiMh1TxUjERERERFJt4RsOpROFSMREREREbnuqWIkIiIiIiLpll0nX1DFSERERERErnuqGImIiIiISLrpC15FRERERESyKVWMREREREQk3XSPkYiIiIiISDalipGIiIiIiKSb7jESERERERHJplQxEhERERGRdMuuFSN1jCRLO37+L1+n4FObC5z2dQo+1Tl/VV+n4HNH41XYF5i74WNfp+BT9avf4+sUfMqZkF0/hqZPy9xlfJ2CXCfUMRIRkSyrY+3HfZ2Cz13vnSIRyXo0K52IiIiIiEg2pYqRiIiIiIikW0L2LBipYiQiIiIiIqKKkYiIiIiIpFuC7jESERERERHJnlQxEhERERGRdLO+TiCTqGMkIiIiIiLpll2/WUtD6URERERE5LqnipGIiIiIiKRbgtHkCyIiIiIiItmSKkYiIiIiIpJu2XXyBVWMRERERETkuqeKkYiIiIiIpJtmpRMREREREcmmVDESEREREZF0S8iek9KpYiQiIiIiIqKKkYiIiIiIpFsC2bNkpIqRiIiIiIhc91QxEhERERGRdNP3GImIiIiIiGRTqhiJiIiIiEi6aVY6kWygXdvmbN2ykh3bVvHc4AEptufIkYOJ349jx7ZVrF41mzJlSgJQuHAhliyaQuyJXXzw/iiPfW6/vSsbwpYQ9sdi5s7+jiJFCnmlLVerdrM6fLRsHB+v/IQej/dKsb3Lg135cOlY3lv4Ia9MGkWxkGIAlK1ajtdnvMUHS1zbGndu4u3UM0zVZrUYsfR9Xln+IW0f65pie6v+HXlp8bsMnf8WA78fTuGQoonbur9wN8MXvcNLS96lz8v3ezPtDFOqeU1uX/EWd6x6h9ABnVNsr9K3Jb2WjKHnwtfoMn04BSsFA+AI8KP5Ow/Ta8kYei16jaCGVbydeqao17wuny//jC9/+YLbH++TYnvPh3rw6dJPGL9oHG9MGkPxkOI+yNJ7ho1+l1s73kG3vo/6OpVM1ajFLcxYNYlZa37g/if6pthep0EtJi76gnWHV9C6U/MU22/Im4eFG2by/OhnvJBtxmjTphmbNi1j69aVDBr0eIrtOXLk4Ntvx7J160pWrpyVeC6sV68Wv/02n99+m8/vvy+gS5d2ifsUKJCfiRPHEx7+Mxs3LuWWW+p4rT1Xo0qzWgxd+h7Dl39A61TOAy36d+TFxe/w/Pw3GfD9MAolOQ90eeEuXlj4Ni8sfJvanRp6M23JJOoYyX9mjHEaYzYaY8KNMWHGmEbu9WWNMdYY82qS2KLGmDhjzEfu5RHGmEHezNfhcPDhB6/RqXNfatRqwe23d6NKlUoeMQ/cfycnT57ipqpNeP/DTxkzeigAFy5c4OURb/Lc8696xPv5+fHeOyNp3aY3deq2YfOW7Qx4POt/SHY4HDw86lFe7TeCp1oNoEmXWylZqZRHzL6t+xjU8RmebvcUq+f+yr0vutp18fzffPD0uwxsPYCR947ggZcfIk/+G3zRjKtiHIY7Rvbno/tGM7LN09zcpTGBFUM8Yv7cdoAxnV/gtdsGs2H+WroPcX1oKl+nMhXq3cio9oN4te2zlKlVgUoNqvqiGf+ZcRgaj+rHvHve5McWz1Gxa4PEjs8le2auYWrrIUxrN5TwcXNp9LKr/VXuagHA1NZDmHPnGzQcfheYa/vyocPh4IlRAxh67zAeavkwzbs2p3Sl0h4xe7bs4YmOT/Fo28f4Zd4qHhza30fZeke3Dm0Y/+6ofw68hjkcDl4Y8yxP3PUsPW+9m/bdW1O+clmPmKiIGF4e+BoLZixO9RiPP/8Qf6zZ4IVsM4bD4eCDD0bRtWs/QkNb0adPF266yfNceN99txMbe4pq1W7lf//7jFGjhgCwdetOGjXqxC233EaXLvfy0Udj8PPzA+Cdd0awePFyatVqyc03t2fHjj1eb9u/ZRyG3iMfYPx9Yxjd5hnqpnIeOLztAG91HsIbtz1H+Pzf6DrkbgCqtqhNyWrleLPDc7zbbSitHu5Mrry5fdEMn0jwwk96GGPaG2N2GmP2GGNeuEJcL/dn03pXOp46RnI1zltrQ621tYAhwJgk2/YBnZIs9wa2ejO55OrfXJu9ew+wf/8h4uLi+PHHWXTp3M4jpkvntnz77RQApk2bS8sWrmrIuXPn+XX1Oi5c+Nsj3hiDMYYbbsgDQL58+YiMjPFCa65OpdBKRB2IIuZQDPFx8ayavZL6bW/xiNmyZjMX3e3dtWEnRYKKABC5P5KoA1EAnIw5waljpyhQOL93G5AByoZW5OjBaI79eQRnnJP1s1dTq+3NHjG71mwl7sJFAPZt2E2hwMIAWCwBOXPgH+CPf44A/Pz9+OvoKa+34WoUD63A6QMx/HXoKAlxTvbMWkvZtnU9YuLOnE/8v3+enFjrut22UKUQIn51/TlfOH6ai6fPUaxWOe8lnwluDL2RyANRRB+KJj4unhU/raBRW88rwOFrNvG3+29ie9gOigUWTe1Q2Ua90BoUyJ/P12lkquq1q/Dn/sNEHIokPi6ehTOX0rxdU4+YqD+j2b19LwkJKW83r1LzRooUK8yaFeu8lfJVu/nmUI9z4ZQps+ncua1HTOfObfnuu6kATJ8+jxYtGgNw/vwFnE4nALlyXX5PyJcvL02a1OfLLycDEBcXx6lTp73VpP+sTGhFjh6M4bj7PBA2ezU1kp0Hdic5DxzYsJuCga5zYWClkuz5bTsJzgQunv+biO0HqdKsltfbcD0zxvgBY4HbgKrAncaYFFcpjTH5gKeA3/7pmOoYSUbJD5xMsnwe2J6kZ3478KPXs0oiOCSQPw9HJi4fjogiODgwzRin08mpU6evODQuPj6eAU8OYWPYUv48GEbVKpX44stJmdOADFQ4sAjHIo8lLh+POk6REkXSjG99exvClv2RYn2lWpUICPAn+mB0puSZmQqWKMzJyOOJyyejjlOwROE04xv3acnW5RsB2B+2m51rtvL6ugm88fsEtq0MJ3pvRKbnnJHyBBXiTNSJxOWz0Se4ISjla71av9bcseodGgy9g19f+gaA49sPUaZtHYyfg3ylilG0RlnyBqf9+rkWFA0swtHIo4nLR6OOUSQw7Ta1v6Md65av90ZqkomKBxUjJvJI4nJM1BGKBRVL177GGJ4Z8QTvjRybWelliuDgQA4nORdGREQRHFwizRin08np038lngtvvjmUsLAlrF+/iCeffBGn00m5cqU5evQEn376DmvXzmPcuDfIkyfrV08KlihMbJLzQGzUcQqUSPuc36BPC7a5zwOR2w9StXkoAblycEOhfFRqWI2CQdn7YklS1gs/6VAf2GOt3WetvQhMBlKOh4RXgTeBC/90QHWM5Grkdg+l2wF8huuFl9Rk4A5jTEnACUQmP4A3mVSG+ly62nXlmLSP6e/vz6MP30u9+u0oVaYOmzZv54Xnn7zqXDNbep6LS5p1b06FmhWZ+cl0j/WFihdi4PvP8L9BH6S5b1b2b56D+t2aUqZmeRZP+AmAYmVKEFgxhBcbPMqQBo9wY6PqVKx/bd1nY1L7cr5Umr/16yVMbvIsv42eTJ2nugGwY/IKzkadoMe8V2k0oi8xf+wmId6ZyRlnsn/xemjVvSWVa1ZiyvipmZ2VZLbUhoCm8/2sz/09WLV0jUfH6lrw38+Frph16zZSp05rGjfuzODBA8iZMyf+/v7Url2dCRO+pUGDDpw9e57Bg1Peu5Tl/Itzfr1uTShdswI/u88DO37ZxLZlG3h6+qv0+/ApDoTtJsF5jb8PXntCgD+TLB92r0tkjKkNlLLWzknPATUrnVyN89baUABjTEPgG2NM9STbF+DqLMUAP6T3oMaYh4GHAYxfARyOjLl/JeJwFKVKXr6HomRIEFFRManGRERE4efnR4EC+Tlx4mTyQyUKrVUNgH37DgIwdersVCd1yGqORx2jaPDlK1tFgopw4siJFHE1m9Si1xN9GNZnCPEX4xPX586bm6FfvszEt79j14adXsk5o52MPk6hJFWOQkFFOHUk5e/6psY1aP9Ed967fUTicxDarj77N+zm73OuYVVbl2+gXO1K7Pl9u3eSzwBno06QN+hyheyGwMKcjU77tb5n1lqajHbdZ2adCax55fvEbV1nvsSp/dde1TCpY1HHKBZ8uVJQLKgoJ2JS/k3UblKbO5+8g0G9BxN3Mc6bKUomOBJ5hBLBlyfRKBFUnKPRx66wx2U161an9i016XNfD3LnyU1AjgDOnz3Hh6+Nz6x0M0RERBQlk5wLQ0KCiIo6kmpMREQ0fn5+5M+fjxMnYj1idu7cw7lz56hW7UYiIqKIiIhi3TpXNWXGjHkMGvRY5jfmKsVGH6dgkvNAwaAinE7lPFC5cQ3aPtGDD5OcBwAWjZ3BorEzALj3gyc5uj8q85POIrwxK13Sz4NuE6y1E5KGpLJbYtfWGOMA3gPuS+9jqmIkGcJauwYoChRLsu4i8AfwLDDtXxxrgrW2nrW2XkZ1igDWrd9IxYrlKFu2FAEBAfTp05XZcxZ5xMyes4h77ukNQM+eHVm2/NcrHjMiMpoqVSpRtKjrA2br1rdeEzec7g7fTVC5YIqXKoF/gD9NOt/KusW/e8SUq1aex8YMYHT/Vzl1/PL9M/4B/rzw6VCWT/+Z1XOv/PxkZQfD91K8bBBFShbDL8CPep0bsWmx59CoktXKctfohxj34Jv8dfzyePkTkceofEsVHH4OHP5+VLqlKtF7rq2hdEfC91GgXCD5ShXDEeBHxa4NOLg4zCMmf7nLw2vKtArltLvz458rB/65cwIQ0rQ6Nj6B2N0+LQhftZ3hOwkpG0yg+2+iWZdmrFm81iOmQrUKDHz9SV56YASxx6+te8okdVs37qB0+ZIElw7CP8Cfdt1asXzRqnTtO3TAK3So15OON/fivZFjmTNlQZbvFAGsXx/ucS7s3bszc+Z4TiwxZ85i+vZ1zVbao0cHli9fDUDZsqUSJ1soXTqESpUqcPDgn8TEHOXw4SgqVSoPQIsWjdm+fbcXW/XfHArfS7GygRR2nwfqdG7E5lTOA3eMfpBPH3yTM0nOA8ZhyFMwLwDBN5Um+KYy7Phlk1fz9yVvTL6Q9POg+ydppwhcFaKkM0eVxHN0Uj6gOrDcGHMAaAD8dKUJGFQxkgxhjLkJ8AOOA3mSbHoHWGGtPZ5aad6bnE4nA/9vGPPmTsTP4eCrr39g27ZdjHh5EOv/CGfOnMV88eVkvv7qQ3ZsW8XJk7Hc1ffyUIA9u9aSP39ecuTIQdcu7bmt451s376bV0e9x7KfpxMXF8ehQxE80P9pH7YyfRKcCXw6fDwvf/sKDj8HS39Ywp+7DnHnM3ezZ/Nu1i3+nX5D7ydXnlwMHuea5OVo5FHG9B9F405NqFq/GvkK5qNlr1YAfPjs+xzYtt+XTfrXEpwJTH7pC578ZigOPwerf1xG1O7DdHq6D4c272XTkj/oOaQvOfPk4qGPXdPwnow4xriH3iRs3lpubFSdYQvfBgtbV2xk89KU92BlZdaZwKrhX9Ph++cwDgc7f1jByV0R1BvUk6Ph+zm4OIzq97UlpEk1EuKd/H3qLMue/gSAXEXz0/H757EJCZyNPsnPA8f5uDVXL8GZwEfDP2b0d6/h8HOw8IdFHNx1kHufvYddm3azdvFaHhr6ILnz5Gb4eNdslUcij/LyAyN8m3gmGvzy66zbsInY2NO06taXx/vfQ89kE9Zc65xOJ2+8+B4fT3oXh58fsybNYd/O/Tz23P+zd+dxNtV/HMdfnzszQrasY0nWCCGRQqJQka3QplJJJSlSvwiVpJ/qp0V7PyXtComUfcgvsi8J2cYyi12yZdz5/v6415gxlisz987ceT895mHuOd9z7ud77p1z7vd8vt/v7crvS1cza8ocqtWuyrCPXqJAofw0bt6Qh57sSodr0k/rnV14vV4ef3wAEyZ8SkREBJ988jWrVv3BwIG9WbRoBT/8MJWRI7/mo49eZ+XK2ezevZe77+4BQIMG9ejTpztJSUkkJyfz2GPPsGuXL8PSq9dARo58k1y5oti4cTPdugV14tl/JNmbzLcDP6L7qH54IjzMGx1D4tqttOzVkc0rNvDbtEW07duZXHlzc+87vmv7nridfPjAK0RERfL4N88DcHj/IT7tNZxkb6BzqUkGWQBUNrPyQBxwG3DHsZXOuT/x3bQHwMxigD7OuVMOELXsODYfkiv1AAAgAElEQVRAsgYz8wIrjj0E+jnnfjCzcsBE51yNE8p3Aeo653qY2XPAfufcq6d7jshcpXP0G7R1dPb4HojMEu3J+oN3M1vto7lCHUJIjbHAujWFsx+WvBPqEELuihp3hTqEkFq1d8uZC4WxbtH6jqA3Y7/OUt+J8H6Zzpn++ezBrZ+dsc5m1hJ4Hd/N+Y+ccy+a2SBgoXPu+xPKxnCGhpEyRvKPOeciTrE8Fl/q8sTlI4GR/t+fy7zIRERERCTcOecmAZNOWDbwFGWbnGl/ahiJiIiIiEjAXJbKX2UcTb4gIiIiIiI5njJGIiIiIiISsHCdZkIZIxERERERyfGUMRIRERERkYApYyQiIiIiIhKmlDESEREREZGAheuXTCpjJCIiIiIiOZ4yRiIiIiIiErBkfY+RiIiIiIhIeFLGSEREREREAqZZ6URERERERMKUMkYiIiIiIhIwZYxERERERETClDJGIiIiIiISMH2PkYiIiIiISJhSxkhERERERAIWrt9jpIaRiIiIiIgETJMviIiIiIiIhClljEREREREJGCafEFERERERCRMKWMkIiIiIiIBSw7TnJEaRpKlRXoiQh1CSG04sivUIYTUlH3xoQ4h5FYXrhzqEEIrPK+9cpbm//ZpqEMIufxlmoQ6hJBZkrQz1CFIDqGGkYiISBZ2RY27Qh1CSKlRJJL1aFY6ERERERGRMKWMkYiIiIiIBCxcezkrYyQiIiIiIjmeMkYiIiIiIhIwjTESEREREREJU8oYiYiIiIhIwJIt1BFkDmWMREREREQkx1PGSEREREREApYcpvPSKWMkIiIiIiI5njJGIiIiIiISsPDMFyljJCIiIiIiooyRiIiIiIgETt9jJCIiIiIiEqaUMRIRERERkYBpVjoREREREZEwpYyRiIiIiIgELDzzRWoYiYiIiIjIWdDkCyIiIiIiImFKGSMREREREQmYJl8QEREREREJU8oYiYiIiIhIwMIzX6SMkeQwzZtfw/LlM1m5cjZ9+nRPtz5Xrlx8+unbrFw5m9mzx3PRRWUAqFu3Fr/++iO//voj8+f/RJs216fZzuPxMG/eJMaO/Tgo9cgIDZrWZ/ycL5kwdzT39bgr3fo6V9bmqykfs2jrbJrd1DTd+vPz5WXqkvH0HdI7GOFmiObNr2HJ0uksXxHDE088nG59rly5+GTUWyxfEUPMrO8oW7ZMmvVlypRi2/aVPPbYAynL3n3vZWJjF7JgweRMjz+j1WtSl09mfcRnc0Zy+yO3pltfs/6lvP/jO0yL/YnGra5Os+7BZ7ry8fQPGTlzBI8OSv+3lB2cS/279evKR9M+4KNpH9C09TXBCjnDNWhan3FzvmT83K+5t0fndOvrXFmLL6Z8xIKts2h2U5N068/Pl5fJS77jX9noPHA2+g8ZRuNWt9Gu80OhDiVD6Vp43BVN6vH57JF8OWcUdz5yW7r1tepfyoif3mPmpik0adU4ZfllDWrz0ZT3U36mrf+Rq69vGMzQJRNkWsPIzLxmttTMVprZMjPrbWYe/7q6ZvbmGbbvYmZvneVz9juXmDOamcWYWV3/75PMrFCI4vjSzJabWa8M3GcTM2uQ6vFDZnZ3Ru0/M3g8Ht54YzBt295D7drX0alTG6pWrZymTJcut7J3759Ur96Y4cP/y+DBfQFYuXINDRrcRP36N9Kmzd289dZLREREpGzXo8d9rFmzLqj1ORcej4d+L/Wh+x1P0L7xHdzQvhkVLi6XpkxiXCIDHhvMj+OmnnQfj/yrGwvnLglCtBnD4/Ew7LVBtG/XhcvrNKdjxzZUrVopTZl7unRi794/qXlpE94aPoIXBj+dZv3QlwcwZUpMmmWfffot7drdk9nhZziPx8Njgx/l6bv60aVpV65r25SLKpdNU2Zb3HaG9n6F6d/NSLO8+uXVqFG3Bvc3f5D7rnuAKrWqUOuqmsEM/5ydS/2vvPYKKteoRNfrH6J7657c+lAn8ubLG8zwM4TH4+Hpl56gxx1PcEvjO096HkiI28azj73IT6c4D3T/1wMsykbngbPVrmVz3hs2ONRhZChdC4/zeDz0frEnfTr35a6m99Gs3bWUq3xRmjLb4rYzpNfLTPtueprlS35Zyn0tHuS+Fg/yWKc+/H3oMPNnLQxm+CGVHISfUMjMjNEh51xt51x1oDnQEngWwDm30DnXMxOeM0s1jFJzzrV0zu0NtLyZZUg3RzOLBho452o6517LiH36NQFSGkbOufecc6MycP8Zrl692qxfH8vGjZtJSkrim28m0Lp1izRlWrduwWeffQvA2LGTaNrUd/fn0KHDeL1eAHLnPg/njieRS5eO5sYbr+Pjj78KUk3OXY3LqrFl41biNsdzNOkoP303jSbXp70jHr8lkbWr1pOcnP70dEnNKhQpVpi5s+YHK+RzVrdubTas30Rs7BaSkpL49tsJ3HRT2tf/plYt+PyzMQCMGzeJJk0aHF/XugWxGzezatXaNNv873/z2b37z8yvQAarWrsK8bHxJGxO5GjSUWaMj6FhiwZpymzbuo0NqzaSnJy204RzjlznRRGZK5KoXFFERkayZ0fAp7cs4Vzqf9HFF7Fs3nKSvckcPnSY9avWc0WTusEMP0PUuOySNOeByd9NT3ceSEg5D6TvOHP8PLAgWCEHXd3al1KwQP5Qh5GhdC087pLLqhIXG0fC5gSOJh1l+viZNLo+7Xkgces21q/agDvJ38AxTVo1Zt7M+fx9+O/MDlkyWVC60jnntgPdgB7m08TMJgKY2RVm9ouZLfH/XyXVphea2U9mtsbMnj220Mw6m9l8f0bqfTOLMLN/A3n8yz4/TbkIMxtpZr+Z2YrTZVH8GZ/X/XH9ZmZX+Jefb2YfmdkCf9xt/cvzmNlX/uzM10CeVPuKNbOi/t8HmNlqM5vqz+b0SfV8Q8xsFvCYmRUzszH+51lgZg1P9/ynMAUo7j8GV5+QxSpqZrH+37uY2Vj/8V5rZi+niv0GM1vsz/xNN7NywENAr1T7fS5VPWqb2Tz/cRhnZhekqt9Q/2vyh5mlvQJnslKlotm6NT7lcVxcAqVKlThlGa/Xy759f1GkyAWA72KyePE0Fi6cwqOP9ku5OLzyynP06zfkpA2IrKp4yWIkxm9Lebw9YQclShYLaFsz44nnHmXYoLNK6IZcqVIl2BqX9vUvme71P14m9eufN28eevd+iCFD3ghqzJmpaMmibE/YkfJ4R+JOipYsGtC2vy9exZJfljFm0dd8u/hrFsxayOZ1mzMr1ExxLvVf//sG6je9gvNyn0eBCwpQ+6raFCtVPLNCzTTFSxZjW/z2lMfbErZT7CzOA72f68Frg97OrPAkk+haeFyx6KJsj091HkjYQdHowM4DqV3XtinTx8/MyNCyPBeEf6EQtMkXnHMb/F3pTrx6rAYaO+eOmlkzYAhwi3/dFUAN4CCwwMx+AA4AtwINnXNJZvYOcKdz7mkz6+Gcqw1gZpecrBywEijtnKvhL3em7m3nO+camFlj4CN/PM8AM5xz9/m3n29m04AHgYPOuZpmVhNYfOLO/I2SW4DL8B3/xcCiVEUKOeeu8Zf9AnjNOTfHzMoCk4FLTvX8zrkDJ4m/DTAx1XE5XV1r++P6G1hjZsOBw8CH+F6jjWZW2Dm328zeA/Y751717/e6VPsZBTzqnJtlZoPwZQof96+LdM5dYWbHMojNTnKMuuFrSBMZeQEREflOF3PATlb31He7zlRmwYKl1KnTjCpVKvHf/w5j8uQYrr22ETt27GTJkhU0bnxlhsQZDCd7G5x4LE7l1ntvZs70uWk+UGUHgbz+Jzswzjn69+/FW8NHcODAwcwKL+iMAI7HKZQqV4qLKpelY73bAXj1y6HUrH8py39dkaExZqZzqf/C2YuoUqsKb41/g7279vL74t9J9n84zFZOfiIIaNNO2fQ8ILoWpnGyj0QB/g0cU6R4YSpWLc+vMeGbOc1Jgj0r3cneggWBT8ysMr5JLqJSrZvqnNsFYGZjgUbAUeByfA0l8GVlTnZmvu4U5SYAFfwf+n/Al1E5nS8BnHOzzayAvyHSAmhzLEMC5AbKAo2BN/3ll5vZ8pPsrxEw3jl3yF+vCSes/zrV782AaqlOUAXMLP9pnn/VGepyJtOdc3/64/oduAi4AJjtnNvor9fu0+3AzAria9zN8i/6BPgmVZGx/v8XAeVOtg/n3AfABwC5c5fNsFsGcXEJlClTKuVx6dIlSUjYftIycXGJREREUKBAfnbvTttFaM2adRw8eJDq1avQoEFdWrVqzg03NOW8886jQIH8fPzx69x77+NkZdvidxCd6g5h8ZLF2J64M6Bta15egzr1a9Gpy83kzZuHqFxRHDxwiDdefDezws0QcXGJlCmd9vVPPOH1j/eXiT/h9a9brzbt2rdk8It9KViwAMnJyRz++2/efy9L9x49rR0JOyieKjtQLLoouxJ3BbTt1Tc05PfFqzh88DAA82cuoFqdS7JVw+hc6g/w+fAv+Hz4FwD0f6svWzfGZXiMmW17/HZKpMp0lShZnB1ncR64rH5NOnW5mTz+88ChAwd588X3MitcySC6Fh63I2EnxUulOg+ULMbObYGfBwCatm7C7B/n4D2aDW+OnIPskxc8O0Gblc7MKgBe0jdiXgBm+jM4rfF9yD/mxA/FDl/j6hP/+KXazrkqzrnnTvaUJyvnnNsD1AJigEeA/54h9FPFcEuqfZd1zq06RfmTxXU6qbM+HuCqVM9T2jn31xme/0yOcvx1z33CutSdY734Gs5Gxs7KeOw5ju0/aBYuXEalSuUpV+5CoqKi6NixNRMnph1QPHHiVDp37gDAzTe3JCbmFwDKlbswZYBp2bKlqVy5Ips2bWHAgKFUqlSfKlUacvfdPYiJ+SXLXwgAVi5dRdkKZShdtiSRUZHc0K4Zs6bMCWjbfo88zw11b6ZlvVsYNugtJn7zY5ZvFAEsWrSMipXKcdFFZYiKiqJDh9b88EPa1/+HSVO5s7MvYd2+fUtmzfK9/i2ad6LaJY2odkkj3n77I1595e1s3SgCWL1sDaXLlyb6wmgioyK5tm0Tfpk6N6Btt8dtp9aVNfFEeIiIjKDWlTXZtDZ7daU7l/p7PB4KFPKNO6lwSXkqVC3Pgmw46Hrl0tWUrVCGUv7zwPXtriMmwPPAM488T8u6t9CqXgdeG/Q2E7/5SY2ibELXwuNWL11NmfKlKek/D1zXtilzpvxyVvto1q4p03JYN7pwFpSGkZkVA94D3nLp+yoUBI7dautywrrmZlbYzPIA7YD/AdOBDmZW3L/vwmZ2bAqRJDM7lnE6aTn/OB+Pc24MMACoc4bwb/Vv3wj4059RmQw8av5Ujpld5i87G193PcysBnCyaZrmAK3NLLeZ5QNanea5pwA9jj0ws9r+X0/1/IGIxZdJA+gQQPm5wDVmVt7/XIX9y/8C0o1I9R+fPanGD90FzDqxXCh4vV4ef3wAEyZ8yrJlMxgzZiKrVv3BwIG9adWqOQAjR35N4cIXsHLlbHr2fIABA/4NQIMG9ViwYDK//vojX3/9AY899gy7du0JZXXOidfr5aV+w3j3y9f47ucvmfL9DNav2Uj3p7pyTYtGAFSvfQlTFn9Hi9bXMuDlpxg767MQR31uvF4vT/QeyPjvR7F4yTTGjJ3IqlVr6T+gFy1b+Xp0fjJyNIULF2L5ihge7Xk/AwcMPeN+R458k5kxY6l8cQX+WDuXu+/plNlVyRDJ3mTeHPAWL3/+EiNnjmDmhNnE/rGJe/vcQ4PmVwFQpdbFjF7wBdfcdDW9//04H0//EIBZP/xM/KZ4Ppr2If+d8j7rf1/P3GnzQlmds3Yu9Y+IiuCNsa/x8Yz/8sTQXrzYcyjJ3ux3/9Tr9TK032u88+Uwxv78BVO+n8GGNRt5ONV5oFrtqvy0eBzNWzflmZef4ttsfh44W08++2/ufLAXsZu3cl27zoyZkP2m5T+RroXHeb3JvNZ/OP/5YiifxXzMjAkxxP6xifv7dKGh/zxQtVYVxiz8iiY3NabP0F6MmjEiZfvoMiUoXrI4S+cuC1UVQiYZl+k/oWCB9qk+6x2beYEV+LrGHQU+BYY555LNrAnQxzl3k5ldha+71Q5gBnCXc66cmXXBN5Pd+UAl4Avn3PP+fd8K9MXXsEsCHnHOzTOzofjG1Cx2zt15snLAIeBjjjcK+zrnfjxFHWLwNwyAAsB9zrn5/oba6/hmZTMg1l+XPP59VwOW+uPu6Zxb6J/koK5zbqeZPQfcDmzy1zvGOfeh//n6OOcW+p+/KPA2vnFFkfi6tD10quc/RR3K4RtjdGxMVVVgNLDff7w7pzredZ1zPfzlJgKvOudizOxGfGO/PMB251xzM7sY+BZfNvVRfF0X9zvnXvU34N4D8gIbgHudc3tS189ft4XOuXIni/uYjOxKlx1VKVTmzIXC2Lp98WcuFOauKFz5zIUkrO09Gj5j2/6J+b99GuoQsoT8ZZqEOoSQqVdE58Gf46afqcdRUHUv1ynTP5+9Ezs66HXOtIZRODixoZKB+83nnNtvZnnxZZm6OefSTdQgahipYaSGkRpGooaRGkaghlFOl9UaRg8HoWH0bggaRsGefEF8PjCzavjG+HyiRpGIiIiISGipYQSY2dtAwxMWv+Gca5IZz+ecuyOj92lm1wMnDojY6Jxrn9HPJSIiIiI5V6jGAGU2NYwA59wjoY7hXDnnJuOblEFERERERM6SGkYiIiIiIhKw7DcPZ2CC9j1GIiIiIiIiWZUyRiIiIiIiEjCnMUYiIiIiIpLTqSudiIiIiIhImFLGSEREREREAhauXemUMRIRERERkRxPGSMREREREQmYxhiJiIiIiIiEKWWMREREREQkYMlOY4xERERERETCkjJGIiIiIiISsPDMFyljJCIiIiIiooyRiIiIiIgELjlMc0bKGImIiIiISI6njJGIiIiIiATMKWMkIiIiIiISnpQxEhERERGRgCWHOoBMooaRZGlLy9YIdQgh9fKRvKEOIaSqnlc81CGE3JKDW0MdQkjFHdgZ6hBCzpscrh9B5Gz8tTUm1CGEVOfLe4c6BMkB1DASERGRLCt/mSahDiHkcnqjSLIezUonIiIiIiISppQxEhERERGRgGlWOhERERERkTCljJGIiIiIiAQsXKeEUcNIREREREQC5py60omIiIiIiIQlZYxERERERCRgmq5bREREREQkTCljJCIiIiIiAQvXyReUMRIRERERkRxPGSMREREREQmYvuBVREREREQkTCljJCIiIiIiAdOsdCIiIiIiIlmEmd1gZmvMbJ2ZPX2S9b3N7HczW25m083sotPtTw0jEREREREJmHMu03/OxMwigLeBG4FqwO1mVu2EYkuAus65msC3wMun26caRiIiIiIikt1cAaxzzm1wzh0BvgLapi7gnJvpnDvofzgPKHO6HaphJCIiIiIiAUsOwo+ZdTOzhal+up0QRmlgS6rHW/3LTuV+4MfT1UuTL4iIiIiISJbinPsA+OA0Rexkm520oFlnoC5wzemeUw0jEREREREJWBb5HqOtwIWpHpcB4k8sZGbNgGeAa5xzf59uh+pKJyIiIiIi2c0CoLKZlTezXMBtwPepC5jZZcD7QBvn3PYz7VAZIxERERERCVhW+B4j59xRM+sBTAYigI+ccyvNbBCw0Dn3PfAKkA/4xswANjvn2pxqn8oYSY51/tWXU/6nD6gw9b8U7tYx3fqC7ZtRad6XlBs/nHLjh1Ow4/Vp1nvOz0PFn0dRYuDDwQo5Q9W4pjZDpr/BSzHDaflwu3TrW9x/E4OnvsbzP/6HPp8/S5HSRVPWFS5VlN6jBjB42usMnvoaRcoUC2boGabWNZfx2oy3eWPWu7R9+OZ061t1bcN/pg3n5Z9ep/8XgyhaOm098+TLw7u/juDeQQ8EK+QMdfW1V/HT3DFMnT+Obj3vSbe+7lWXMW76Z/yeMI/rW1+XZt1/v36Thetm8v7nrwUr3AzRvPk1LFk6neUrYnjiifR/u7ly5eKTUW+xfEUMMbO+o2zZtBMYlSlTim3bV/LYY77XvHTpkkz68UsWLZ7GgoVT6N793qDU41w0b34Ny5fPZOXK2fTp0z3d+ly5cvHpp2+zcuVsZs8ez0UX+Y5B3bq1+PXXH/n11x+ZP/8n2rQ5fk4sWLAAX3zxHsuWzWDp0unUr18naPU5W5lRfwCPx8O8eZMYO/bjoNQjGPoPGUbjVrfRrvNDoQ4l0+T060B255yb5Jy72DlX0Tn3on/ZQH+jCOdcM+dcCedcbf/PKRtFoIaRnCMza29mzsyqhjqWs+LxUOLZ7mx9YCAbWj5EgZuuIVfFC9MV+2vSbGLbPkps20f585vJadYVffxuDs7/LVgRZyjzeOg8qCuvdXmR/s17Ub9NI0pVSvsBcPPvGxnU+l88e+MTLPxxLh373pWyruuwR/npg/H0b/Y4L7Tty187/wx2Fc6ZeTzc98KDvHTPIHo3e5SGba6mdOW0xyB25Qb63vQET93wOL9O+oU7+6ZtPHR64g5+/3VlMMPOMB6Ph2f//S8euK0nLRt25Kb211Px4vJpyiRsTeTpR59j4pjJ6bYf8danPNl9YLDCzRAej4dhrw2ifbsuXF6nOR07tqFq1UppytzTpRN79/5JzUub8NbwEbwwOO33BQ59eQBTpsSkPPZ6j9Kv72Aur9OMpk3a0+3Bu9LtMyvxeDy88cZg2ra9h9q1r6NTpzZUrVo5TZkuXW5l794/qV69McOH/5fBg/sCsHLlGho0uIn69W+kTZu7eeutl4iIiADgP/95jqlTY6hV61rq1buB1avXBb1ugcis+gP06HEfa9ZkzXr/U+1aNue9YYNDHUamyenXgXORFb7HKDOoYSTn6nZgDr5+ndlG7poXc2RTPElbEiHpKPt+mE2+ZlcFvP151SsRWbQQB+cszsQoM0+F2pXYvimRHVu24006yq8T/kftFvXSlFk9dyVHDh8BYMOStVwQXQSAUpXKEBHh4fc5ywH4++DhlHLZSaXaldkWm8D2LdvwJh3llwlzqNe8fpoyK+f+llK3tUvWUKRkkZR15WtUpFDRQiyfvTSocWeUmnWqsyl2C1s2xZGUdJQfvptCsxvTTtYTtyWBNb+vI9klp9t+7s8LOLD/YLrlWVndurXZsH4TsbFbSEpK4ttvJ3DTTS3SlLmpVQs+/2wMAOPGTaJJkwbH17VuQezGzaxatTZlWWLiDpYu9X0o2r//AGvWrKdUqegg1OafqVevNuvXx7Jx42aSkpL45psJtG6d9hi0bt2Czz77FoCxYyfRtGlDAA4dOozX6wUgd+7zUj645M+fj0aNruDjj78CICkpiT//3BesKp2VzKg/QOnS0dx443UpxyBc1K19KQUL5A91GJkmp18HJD01jOQfM7N8QEN888Lf5l/mMbN3zGylmU00s0lm1sG/7nIzm2Vmi8xsspmVDFXsUSWKcDRxZ8rjo4k7iSpRJF25/C0aUu77tyn1Zj8io/1dycwo8XRXtg8dEaxwM1yhEoXZHX+8/nsSdnFBicKnLH91p2tZEbMEgBIVSnJw30Eeee9Jnv3hFTr2vQvzZL9TSeHowuxKOH4MdiXs4oLoUx+Dprc2Y2mMryFsZtzV/14+G/JJpseZWUqULE5i3LaUx4nx2ylRsngII8p8pUqVYGvc8QmL4uISKFmqxCnLeL1e9u37iyJFLiBv3jz07v0QQ4a8ccr9ly1bhlq1qrFgQdb9kFSqVDRbt6Y9BqXSHYPjZVIfA/A1LBYvnsbChVN49NF+eL1eypcvy44du/nww/8wb94k3n13KHnz5glepc5CZtQf4JVXnqNfvyEkJ6e/iSBZV06/DpyLZFym/4RC9vs0I1lJO+An59wfwG4zqwPcDJQDLgW6AlcBmFkUMBzo4Jy7HPgIeDEUQeMLKP2yE9K2f838lfVNuxDb5hEO/rKUkkOfAKDQna3YP2thmoZVdmMnqf+p0tZXtruacjUr8tMH4wHwRERQuV5VRr/4CS+0+RfFypagUYcmmRluprCTff3BKc7DjdpfQ8VLK/H9++MAaHH3jSyduSjNBTW7OfmfQOgH02amgN73pyjTv38v3ho+ggMHTp4lO//8vHzx5bs89dQg/vprf4bEmxkCOQanK7NgwVLq1GlGw4atefLJRzjvvPOIjIzksstq8MEHn3LllS05cOAQTz6ZfuxOVpAZ9b/xxuvYsWMnS5asyJygJdPk9OvAuXBB+BcKmpVOzsXtwOv+37/yP44CvnHOJQOJZjbTv74KUAOY6r/oRAAJJ9up/5uNuwE8X7w6nQqWzfDAkxJ3Hs8AAZHRRUnavjtNmeS9f6X8vnf0TxR70jeoOk/tS8hbtzoX3NEKOz83FhVF8sFD7Hh1ZIbHmVn2JO6icKnj9b+gZBH2bt+Trly1hpdyU49bGHrrQI4eOZqy7ebfY9mxxTfr5ZIp86l42cX8PHpGcILPILsSd1Gk5PFjUKRkEfZs252u3KUNa3Jzjw4816l/yjG4uE4VqtarRvO7biT3+bmJjIrk8IHDfDn006DFf64S47cTXfr4nfLoUsXZnrgjhBFlvri4RMqULpXyuHTpkiQmpJ29Nd5fJj4ukYiICAoUyM/u3XupW6827dq3ZPCLfSlYsADJyckc/vtv3n9vFJGRkXzxxXt8/dV3fD8+/XisrCQuLoEyZdIeg4QTjsGxMnEnHIPU1qxZx8GDB6levQpxcQnExSWkZMrGjZtEnz5Zc1KazKh/gwZ1adWqOTfc0JTzzjuPAgXy8/HHr3PvvY8HpU7yz+X064Ckp4aR/CNmVgS4FqhhZg5fQ8cB4061CbDSOXfGgTypv+l49cUtM+WWweEVf5CrXCmiypQgadsuCrRqTHzvl9OUiSh2Ad4dvsZCvuvqc2T9FgAS+rySUqZg+/S+StIAACAASURBVGbkvrRytmoUAWxcto4S5UpStExx9mzbTf3WDXm/5+tpypStXp67hzzIsHsG89eufam2Xc/5Bc8nf+EC/LV7H5c0qEHs8g3BrsI5W79sLdHlS1LswuLsTtxNg9aNeLPnsDRlylUvT9eXuvPS3c+zb9fxCSaGP3Z8JrZrOlxLhZoVs93FcMWS3ylX/kLKlC3FtoTttGrXgt4P9Q91WJlq0aJlVKxUjosuKkN8/DY6dGjNvff2TFPmh0lTubPzLcyfv5j27Vsya9YvALRo3imlTL9nHufA/gO8/94oAN59dyhr1qxj+PCs37124cJlVKpUnnLlLiQuLpGOHVtzzz1pj8HEiVPp3LkDv/66mJtvbklMjO8YlCt3IVu2xOP1eilbtjSVK1dk06Yt7Nq1h61bE6hcuQJr126gadOGacZhZSWZUf8BA4YyYMBQABo3vpLHH39QjaJsIqdfB85Fcpj2MFDDSP6pDsAo59yDxxaY2SxgJ3CLmX0CFAOaAF8Aa4BiZnaVc26uv2vdxc650Ezl4k1m26B3uXDEYIjw8Oe3UziybjNFe3bm8G9r2T/jVwrf3ZZ819bHeb149/5FwtPDzrzfbCLZm8xnA/9L71H98UR4mDN6BvFrt9Ku163ErljP0mkL6dT3Ls7Lm5vu7/i6EO6K28nwB4bikpP5+sVR9Pn8Wcwg9rcNzPpqWohrdPaSvcl8NPBD+o16Fk9EBDGjp7F17RY69r6dDcvXsWjaAjr360LuvLnp9c5TAOyM38ErXYeEOPKM4fV6GdT3FUaMHk6EJ4Jvv/yedWs20PNfD/Lb0lXMmDybS2tX4+1PXqFAwQI0bXE1PZ/qRqurbwXgiwkfUqFSOfKen4fZy36g3+MvMGfmvBDX6vS8Xi9P9B7I+O9HERERwahRo1m1ai39B/Ri8eIVTPphGp+MHM1/Rwxj+YoY9uzZyz13P3rafV51VV3uuPMWfluxirnzJgHw3LMvM3lyTBBqdPa8Xi+PPz6ACRM+JSIigk8++ZpVq/5g4MDeLFq0gh9+mMrIkV/z0Uevs3LlbHbv3svdd/cAoEGDevTp052kpCSSk5N57LFn2LXLd/OoV6+BjBz5JrlyRbFx42a6desTymqeUmbVP1w9+ey/WbBkOXv37uO6dp3pfv9d3NL6+jNvmE3k9OuApGfh3qdcMoeZxQD/ds79lGpZT+ASfNmhxsAfwHnAMOfcVDOrDbwJFMTXKH/dOffh6Z4nszJG2cXLR/KGOoSQOuCOhjqEkFtycGuoQwipuAM5s/9+al4N6M/x/toaE+oQQq7z5b1DHUJIfb3pu5MMiAqdq0tfl+mfz36Omx70OitjJP+Ic67JSZa9Cb7Z6pxz+/3d7eYDK/zrl+JrMImIiIiIZClqGElmmGhmhYBcwAvOucRQByQiIiIiGSNU02lnNjWMJMOdLJskIiIiIpKVqWEkIiIiIiIBC9eMkb7gVUREREREcjxljEREREREJGDhOqu1MkYiIiIiIpLjKWMkIiIiIiIB0xgjERERERGRMKWMkYiIiIiIBMwpYyQiIiIiIhKelDESEREREZGAaVY6ERERERGRMKWMkYiIiIiIBEyz0omIiIiIiIQpZYxERERERCRgGmMkIiIiIiISppQxEhERERGRgIXrGCM1jEREREREJGD6glcREREREZEwpYyRiIiIiIgELFmTL4iIiIiIiIQnZYwkS+t7OCLUIYTUw3/n7D/ROw4tD3UIIRdhOfv+Vfn80aEOIeSuzXNRqEMIqSVJO0MdgmQBny0aFuoQJJVwHWOUsz91iYiIiGRxnS/vHeoQQkqNIgkWNYxERERERCRgGmMkIiIiIiISppQxEhERERGRgIXrGCNljEREREREJMdTxkhERERERAKmMUYiIiIiIiJhShkjEREREREJmMYYiYiIiIiIhClljEREREREJGAaYyQiIiIiIhKmlDESEREREZGAaYyRiIiIiIhImFLGSEREREREAuZccqhDyBTKGImIiIiISI6njJGIiIiIiAQsOUzHGKlhJCIiIiIiAXOarltERERERCQ8KWMkIiIiIiIBC9eudMoYiYiIiIhIjqeMkYiIiIiIBExjjERERERERMKUMkaSY112TR3uf+4BPBEepn01lbHvfJtmfZuubWl2ewu8R73s272Pt/q8wY64HZSrVp6HXuxOnvx5SfZ6+fat0fxvwpwQ1eKfK9K0FlUH34NFeNj6+Qxih39/0nIlbqpPrRG9mNeiH/uWbSD6loaU6946ZX3+amWZ16wvf63cFKzQ/7Frm13NkKHP4ImI4LNPvuHN1z5Isz5Xrijeef8Val5WnT2799K1y+Ns2RwHQLXqVfjPG4PInz8fycnJNG9yC3//fYTxP3xKiehiHDr0NwAd293Lzp27g163QDW9rhGDhz5DRISHz0d9y/DXPkyzPleuKN56fyg1a/uOQbd7e7Nlcxy3dLyJ7j3vTylXrUYVmjW+mZUrVjN24ihKRBfj8KHDANza/v4sfQxSa9j0Sp4e3IuICA9jPv+eEcM/TbP+8itr868XenFxtYo8+eAApk6cCUDJMtG8/tG/iYjwEBkZyRcjvmH0qHGhqMI5ueSaWtw8sAueCA9zv57BtHfHp1nf9P5WXHXbtXiPetm/ex9fPPUee+J2AtDm6Tuo1rQOAJOHj2HJxLlBj/9cXdGkHo8NegSPx8PELyfx+dtfpVlfq/6l9Hz+ESpcUoHnuw8m5ofZAFzWoDaPPvdwSrmyFcvyfPfB/Dz5f0GNPyPUuuYyujzbFU+EhxlfTWX8u2PTrG/VtQ3X3tY85Vr43pPD2Rm3I2V9nnx5GDb9LeZPnsfHAz88cffZXv8hw5j9v/kUvqAQ3332XqjDyTKSwzRjpIZRDmdmRYDp/ofRgBc4dsa7wjl3JBOesw5Q3Dn3U0bvO1Aej4dugx/iuTsHsCthFy9PGMb8qb+yde2WlDIbVm6gT6veHDn8N9d3vpG7+93Lfx55mSOH/uaNXsNIiE3gghKFefWH11gyawkH9x0IVXXOnse45N/3sajTixyO38WVk4ewY/IiDvwRl6ZYxPm5Kdv1BvYuWpuyLHHM/0gc47v457vkQmp/0idbNIo8Hg9D//MsHdreS3xcIlNjxvDTpOn8sWZ9Spk77+7I3r1/ckXt5rS/pRXPPv8kXe99nIiICN798BW6d3uKlb+t5oLChUhKOpqy3UNd+7B0yW+hqNZZ8Xg8/Ps/A+nU7j7i47YxeeY3TJ40I80xuOPuDuzdu48rL7uedre0ZMDzT9Dt3t6M+WYiY76ZCMAl1S7mky/fZuWK1SnbdX/gSZZlg2OQmsfjof+/+/BAp54kxm/n68kfM3Pyz2z4IzalTELcNvo/9gJdHr4jzbY7tu2k800PkHQkiTx58/DdrC+YOflndmzbGeRa/HPmMToOuo+3O7/I3sRd9Pn+JX6bupDEdcfPA1t/j+WV1n1JOnyERp2b07bvnYzs8QbVml5GmerlebnlU0TmiqLn18+yKmYph/cfCmGNzo7H46H3iz3pdftT7EjYwYeT3uF/U+YSu/b4+Wxb3HaG9HqZ2x7qmGbbJb8s5b4WDwKQv1B+vpozivmzFgY1/oxgHg/3vfAgL975LLsSd/HS96+wcNp84tZuTSkTu3IDfW96giOHj9C88w3c2fce3ujxasr6Tk/cwe+/rgxF+EHRrmVz7rilDf1eePXMhSXbU1e6HM45t8s5V9s5Vxt4D3jt2ONAGkVmFvEPnrYOcMM/2C7DVK5dmYTYBLZt3sbRpKPMmTCbK1rUT1Pmt7krOHLYlwX4Y8kaipQsAkD8xngSYhMA2LNtN3/u/JOChQsEtwLnqGCdShzcmMihTdtxSV4Sv/uF4jfUTVeu0tOd2Pj2BJIPJ510P9HtG5I47pfMDjdD1Klbk40bNrEpdgtJSUmMG/MDN7ZqlqbMja2u46svfXf9v//uJ65uchXgy7L8vnINK3/zNQT27N5LcnJycCuQAepcXpONGzazKXYrSUlJfDd2Eje0ui5NmRtaXsfoL74DYMJ3k2l0zVXp9tO+QyvGfftDUGLOTJfWqcbmjVvZuimeo0lH+fG7qVx7Q+M0ZeK3JPDH7+tITk57d/Ro0lGSjvj+LnKdF4XHY0GLO6NcVLsSOzZtY9eW7XiTvCye8AuXtqiXpszauStJOuy7FMQuWUuhaN95MLpyGdb9uopkbzJHDv1N3KpNXHJNraDX4VxccllV4mLjSNicwNGko0wfP5NG1zdIUyZx6zbWr9qASz713fEmrRozb+Z8/vZfL7KTSrUrsy02ge1btuFNOsovE+ZQr3naa+HKub9xxP8eWJvqWghQvkZFChUtxPLZS4MadzDVrX0pBQvkD3UYWY4Lwr9QUMNITsnMJpjZIjNbaWZd/csizWyvmQ02s/nAFWbWxszWmNnPZjbczL7zl81nZiPNbL6ZLTGz1maWBxgI3GlmS82sQyjqVji6CDvjj9/Z3ZWwiyIlipyyfLNbm7N45qJ0yyvXqkxUVCSJmxIzJc7Mkju6MIfjd6U8Phy/m/OiC6cpk79GOXKXKsLOqYtPuZ/otleROC57dB0pWbIE8VuPv07x8YmULFUiXZm4rb5Gr9frZd++vyhc+AIqViqHczB63AhmzB7Ho491TbPdm++8xMw543niqe6ZX5FzEF2qBPFxCSmP4+MSiS554jEoTlzc8WPw176/KFy4UJoybW++MV3D6I23hzD953H0evJhsovi0cVIjN+e8nhb/HaKRxcLePvoUsUZO/Mzpi3+nhFvfZqtskUAhUoUZm+q88DehF0ULHHBKctf2akpv8f4PgDHr9pEtSa1icqdi/MvyE/lq6pTqGTRTI85IxWLLsr2+ONdwnYk7KBo9NnX4bq2TZk+fmZGhhY0haMLsysh7bXwghOuBak1vbUZS2N81wQz467+9/LZkE8yPU6RYFFXOjmde5xzu80sL7DQzMYAfwEFgcXOuf7+dX8ADYHNwOhU2w8EfnLOdTGzC4BfgZrAIKCGc+7xYFYmNbP0d3dPNcPKNe2bULFmJfp36ptm+QXFL+Cx13vzZu/Xs9/sLCe9uZ2qDmZUGXQ3vz327il3UbBOJbyH/mb/6q2nLJOVBPKan7QMjsiICOpfWYfmTTpw6NAhxk74hKVLV/LzrLk82LUPiQnbyJfvfD7+bDidbm/H6C+/y7R6nIuTVA9OfO+e9Dgd/73O5TU5dPAwq1cd717Z/YE+JCZs5/x85/PRp2/S8ba2fPPV+HT7yWpO/noHLjF+Ozc37UyxEkV585OhTJ04k107ssfYKuCMr3Vqdds1omzNirx563MArP55OWVrVqTX2BfYv2sfsYvXkuz1ZmKwmSCQv4czKFK8MBWrlufXmAUZE1OQ2ckOwikOQaP211Dx0ko8d+szALS4+0aWzlyUpmElOUe2+9wTIGWM5HR6mdkyYC5QBqjoX34EODbKuBqwxjm3yfn+Sr5MtX0L4BkzWwrMBHIDZc/0pGbWzcwWmtnC2P2ZM3ZlV8JOipY6fmewSMki7N6e/gNNzUa16NCjEy/dP5ijR46PKcmTLw/PfPwsX7z6GX8sWZMpMWamwwm7yV3qeIYsd6nC/J24J+VxZL7c5KtahnpjB3L1guEUvLwStUf1oUCtCillots1yDbd6MCXISpVJjrlcalS0SQmbE9XpnSZkgBERERQoEB+9uzeS3z8Nn753wJ2797DoUOHmTZlFrVqVQMgMWEbAPv3H2DM6AnUubxmkGp09hLitlGqdMmUx6VKR5OYmPYYJMRvo3Tp48cgf4H87NmzN2V9u1taMm5M2mzRseN4YP8Bxn4zkcuy8DFIbVvCdqJLFU95XKJUcXYk7jjNFie3Y9tO1q3eSJ362asr2d7EXRRKdR4oVLII+7bvSVfu4oaX0qLHzXzQ9eU058Epb4/j5Zb/4p27XgSDHRsT0m2ble1I2EnxUsczhMVKFmPntl2n2SK9pq2bMPvHOXiPZrNGod+uxF0UKZn2WrhnW/pr4aUNa3Jzjw683HVIynvg4jpVuP6elgyf8wGdn+lC45ubcvu/7gpa7CKZQQ0jOSkzawY0Bq50ztUCluNr2AAccsdvFZyuY70B7VKNWSrrnPvjTM/tnPvAOVfXOVe3XL6LzqUap7R22VpKli9F8QtLEBkVSaPWjVkwdX6aMuWrV+Dhlx5hyP0v8OeuP1OWR0ZF8vSHzxAzdga//JA9upGdaN+S9eStEE2essWwqAii2zVg++TjXQWP/nWImGrd+Lneo/xc71H+XLSOpXe/yr5lG3wFzCjRuj6J32WfhtGSRSuoUKEcZS8qQ1RUFO1vacVPk6anKfPTpBncdnt7ANq0u4GfZ/lm2Zox/WeqV69Cnjy5iYiIoEHDK1izZj0REREULuzrehQZGUmLG5qy+vczvsVDZsniFVSoeBFlLypNVFQU7W5uyeRJM9KUmTxpBp3uaAdA63bXM2f2vJR1ZkbrdjfwXaqGke8Y+LraRUZG0vyGJqxelXWPQWq/LVlF2QoXUrpsSSKjIrmxXXNmTv45oG1LlCzGebnPA6BAwfxcdkVNYtdvzsxwM9zmZespVi6awmWKEREVQZ3WDVgxNe0EAmWql+O2IV35sOvL7N+1L2W5eYy8hfIBUKpqWUpVvYjVPy8PavznavXS1ZQpX5qSF0YTGRXJdW2bMmfK2Z3TmrVryrRs2o0OYP2ytUSXL0mxC4sTERVJg9aNWHjCtbBc9fJ0fak7L98/hH2proXDH3uNRxo8wKONuvHZiyOZPXYmXw5NO6ujhK9kXKb/hIK60smpFAR2O+cOmVl1oN4pyq0EqpjZhcBW4NZU6yYDPYHHAMzsMufcEnzd8UI6kjHZm8yHA97j2U+fxxPhYfrX09jyx2Zu730n61asZcHU+dzzzL3kzpubJ999GoAd8Tt46f7BNLypEdWuqE7+Qvm5toNv4PqbT7xO7O8bQ1mls+K8yazu+zF1vuqHRXiI+3ImB9ZspeJTHdm3bAM7JqcfT5XaBVddwuGE3RzatP205bISr9fL008O4ptxI/BERPDFp9+yZvU6nn6mJ0sX/8ZPP87g81Hf8M4HrzB/6VT27vmTB+7tBcCfe/fx7tsfMzVmDM45pk2ZxdTJMeTNm4dvxo0gMiqSiIgIZsX8wqiRo88QSeh4vV769nmBr8aOICLCw5efjWHN6nU81e9Rli35jck/zuSLT7/lrQ9eZt6Syezd8ycP3tc7ZfurGtYjIT6RTbHHu0+ed14uvho3gqjISDwRHn6OmctnI78JRfXOmtfrZUjfV3n/qzeIiPAw7suJrF+zkUeeeoCVy1YTM/lnatS+hNc/HkqBQvlp0qIRjzz5AO2uuYMKlcvz5PM9cc5hZox893PWrlp/5ifNQpK9yXw78CO6j+qHJ8LDvNExJK7dSsteHdm8YgO/TVtE276dyZU3N/e+4/tb2BO3kw8feIWIqEge/+Z5AA7vP8SnvYaT7M1eE5J4vcm81n84//liKB6Phx++/pHYPzZxf58urF62hv9NnUvVWlV4ccTz5C+YjwbNr+K+J+7h7mt909ZHlylB8ZLFWTp3WYhr8s8le5P5aOCH9Bv1LJ6ICGJGT2Pr2i107H07G5avY9G0BXTu14XceXPT652nANgZv4NXug4JceTB8+Sz/2bBkuXs3buP69p1pvv9d3FL6+tDHZZkEgvXPoJy9szsOWC/c+5VM8sNjMc3hfdqoCTQD5gH7HTOFUq1XTtgKL5pvhcAhZ1z95jZ+cDrwJX4spPrnHNtzawY8CMQAbzonEv7BUKptC/bOke/QR/++/xQhxBSdxw69cQPOUWE5ezEfrHchc5cKMxdmydzMufZxZIkjWEpFZmzZ0X7bNGwUIcQclFFK2SpqS+LFrg40z+f7dz3R9DrrIyRpHDOPZfq98PAqW6JnPhJZZpzror5RjK/Dyz07+MA8MBJnmcHkH5uaBERERGREFHDSDLCw2Z2J3AevkZR+H31tYiIiIgAkBymPc7UMJJz5px7BXgl1HGIiIiIiPxTahiJiIiIiEjAwnWOgpw9qldERERERARljERERERE5CyE6nuGMpsaRiIiIiIiEjB1pRMREREREQlTyhiJiIiIiEjAwnW6bmWMREREREQkx1PGSEREREREAubCdPIFZYxERERERCTHU8ZIREREREQCpjFGIiIiIiIiYUoZIxERERERCZi+x0hERERERCRMKWMkIiIiIiIB06x0IiIiIiIiYUoZIxERERERCZjGGImIiIiIiIQpZYxERERERCRgyhiJiIiIiIiEKWWMREREREQkYOGZLwIL11SYSEYws27OuQ9CHUco5fRjkNPrDzoGqn/Orj/oGOT0+oOOQU6hrnQip9ct1AFkATn9GOT0+oOOgeovOf0Y5PT6g45BjqCGkYiIiIiI5HhqGImIiIiISI6nhpHI6ak/sY5BTq8/6Bio/pLTj0FOrz/oGOQImnxBRERERERyPGWMREREREQkx1PDSEREREREcjw1jERE5JTM5/xQxyEiEkxmdnMgyyS8qGEkcgIzO9/MPP7fLzazNmYWFeq4RILFzEaZWQEzywusBDaaWe9QxyUiEkT9T7LsmaBHIUEVGeoARLKg2cDVZnYBMB1YCNwK3BnSqILIzC4GngQuItV5wjl3bciCCiIzM3yvdwXn3CAzKwtEO+fmhzi0YLnUObfPzO4ApgBP4fs7GBbasILHzIoBDwDlSPs3cF+oYgqGMzWAnXM56T3QEHiO4+dBA5xzrkIo4woWMysBDAFKOeduNLNqwFXOuREhDi1Tmdn1wA1AaTNL/X4vACSHJioJFjWMRNIz59xBM7sfGO6ce9nMloQ6qCD7BngP+BDwhjiWUHgH3wXwWmAQ8BcwBqgXyqCCKJeZRQJtgXedc0fMLKd9IBgP/AxMI2f9DeQPdQBZyAigF7CInPUeOGYk8DHHsyR/AF/jOy7hbDvwG3AYX8b8mL+Ap0MSkQSNGkYi6ZmZXYUvY3C/f1lO+1s56px7N9RBhFB951ydYw1i59weM8sV6qCC6L/AZnwfDmb5M2b7QxtS0OV1zv0r1EEEm3Pu+VDHkIX86Zz7MdRBhFBR59xoM+sL4Jw7amZh30B0zi0BlpjZ5/hukJV1zq0LcVgSJDntw55IIB4D+gLjnHMrzawCMDPEMQXbBDPrDowD/j620Dm3O3QhBVWSmUUADlK6VeWYjIlz7jXgtWOPzWwLvuxZTjLRzFo65yaFOpBgMrM3T7feOdczWLFkATPN7BVgLGnPg4tDF1JQHTCzIhw/D14J/BnakILqOnzdh3MB5c2sNvCsc659aMOSzKQveBU5gZl1dM59c6Zl4czMNp5kcU7qW38nvnFldYBPgA5A/5zyHjCzHsAo/zij94HLgL7OuekhDi1ozOwv4Hx8H4iTOD6+pEBIA8tkZnYEX6ZwNBCPr94pnHOfhCKuUDCzk90QczlorGUdYDhQA997ohjQwTm3PKSBBYmZLcLXOJrpnLvMv2yFc+7S0EYmmUkNI5ETmNli51ydMy2T8GZmVfFdFA2Y7pxbFeKQgsbMljvnappZC6An8CzwgXPu8hCHJpnMnyHoiO/GwFF8Y0rGOOf2hDQwCQn/WMMq+M6Da5xzSSEOKWjMbJ5z7kozW5KqYbTcOVcz1LFJ5lFXOhE/M7sRaIlvJprU3UkK4PuAkGP4pyd/GGjsXxQDvJ8TLor+qdqXO+dqAKtDHU+IHLtjdiPwsXNu0bEp7MOdmVV1zq323y1PJ9y7UTnnduGbeOU9MysN3A6sNLN/Oec+DW10wWVmBfHdFDh2HpwFDHLO5YjuZCf5zp6LzexPYIVzbnsoYgqyVWbWCfCYWXl83eznhTgmyWRqGIkcF49vSuI2+GYhOuYvfDMT5STvAlH4ZmcDuMu/rGvIIgoS51yymS0zs7LOuc2hjidElpnZJOBi4Bkzy8fxxlK4ewLfNN3/Ock6Rw4Za+VvGN4ONAd+JO05Maf4CF8Xsk7+x3fhm6Utp3zJ5/3AVRwfY9sEX8PgYjMblAMayj2AgfjGl44DJgP9QhqRZDp1pRM5gZlF5YTMyOmY2TLnXK0zLQtXZjYD39Tc84EDx5Y759qELKgg8k88cTmwzjm328yKAhf6Z2uSMGZmzwM3AauAr4CfnHM5KmN+jJktdc7VPtOycGVmE4Cuzrlt/sclOH6DbLY/qy4SVpQxEknvCjN7jhz6pX5+XjOr6JxbD+CfmS/sp2lNJUdPWez+396dR0tW1Wcf/z7NPA8yiBrGgIiMjS2DCAEVFQFRQCWikZg4oAyB+L6SiGThgEHjAPFFBEMIUwQBwTi1QWZk6m7oBpGAoCgaQeY0MjQ87x/7VHf1reomsqyzr3Wez1p31T3n0K6n7Xurzu/svX/bfqb5N38d8ClgOaArU+kWOxpg+4K2slRyNHAXsFXz9emy3/H898Eura/4naSdbF8F8zd8/V3lTG1av1cUNe4DNmkeloz9w0NJFzI4Uv4IZWbJKbafaj9VjFoKo4hBXd/UD+AjlFa1d1FuiNYDDqobqT22L6+doSZJ/0yZSrkzpTCaS1l30oUNbvdazDVTWjePsw1qB5hEPgic3qw1EvAg8J6qidp1paT/oGz4DbAvcIWkFYCH68VqzS+AFwLnNMdvp/wMbEnZ/PwvKuWKEcpUuogJJF1ne7vaOWqTtAwLuhH9xPaTz/FHxkbTqrn35rg0pUiYO+6tmnt6XRgndGPqzFTKWFgzlfIBd/SGQdLKALYfrZ2lTSpDhW8FdmpOPQCsY/tD9VK1R9LltnfpOxZwue2dJf3Y9mYV48WIZMQoYlBnN/WTtJvtHw6ZTrSRpC5MIwLA9kr9x5L2AV5ZKU4NTzdd6HobO76ADm1wCyDp48PO2z627Sxtajbx/AzlfoMe2QAAIABJREFUyfgngDOANSidud5t+3s187VB0oG2z5R0xITzANj+fJVgLbNtST8FtqM0oLgbOL9uqlatLekltn/ZHL+IspcT9N0bxHhJYRQxqDda9Iq+c13pRrUL8EOGTyfqwjSioWx/U9JHa+do0ZcpN0BrNovx30b31l3N7ft+WRY0JBh3/0zpvLUK5b3gjbavbfb1OgcY+8KIsrEvwEpDro39qJmkTYB3ULoSPkDZy0q2d60arH3/B/iRpJ9QZk5sAny4mUp4VtVkMTKZShcRAyRtYPvu5zo3riaMmE2hFMm72N6hUqTWSXo58FrKDcF/2r6lcqSqmqmlF9t+fe0so9TfdU3SbbZf1ndt/tTKLpD0KttXP9e5cSPpWeBK4L2272zO3dWlBkTNiPk0YDawGeV98FbbXWq+0UkZMYoYQtKbgJdTnhQD4z+FZoLzgYkbXH6D0sK5C/pHzOYBPwPeXCdKNT8G7qf5nJD0Itu/qhupquWBLtwY9k+ZnHgT2LUnqScy+D447Ny42ZcyYnSppO9R2rarbqR2NfvZfcn29nRzD6/OSmEUMYGkr1BugnYFTgX2o+xnM/aa6TIvB1aZMGqyMn1F4riz3ZkOfMNIOhg4ljKN5hmaVs2UJ6edIGkOCwqBJShrC7rwcGQrSY9S/s2Xa76nOe7Ee4CkHYAdKVNJ+9cZrUz5WRhrti8ELmymjO1D6dK6tqSTgAttT68asD0/kPRm2xfVDhLtyVS6iAkkzba9Zd/risAFtnevnW3UJL2Z8kG4N3Bx36XHgH+3fU2VYC2TdDzwScoT8+9R9nM53PaZVYO1RNKdwA6276+dpRZJ6/UdzgN+09WNTrtG0i7AnwEfoLSp73kM+JbtO2rkqknS6sD+wNttd2G9LZIeoqy1e5LyWdDby2v1qsFipFIYRUzQa9ct6VpKq9IHgFtsb1w5Wmsk7WD7R7Vz1NJbZyHpLSx4YnppV9pVS7oMeI3tru7jhaSNgF/aflLSn1H2Lvk3213YvyUoxbHtn9fOEXVIGjo62OX3xS7IVLqIQf8haVXgs8BMynSaU+tGat0sSR9icJ3VX9aL1Kqlmtc9gHOand5r5mnbncAPm80d+1vWn1AvUuvOB14h6U8pmz5fDJxN+ZmIbjhV0v69YljSapSR87FuwBGF7WeazX03YuFppJ2YOdFVKYwiJrD9iebb85sbw2VtP1IzUwVnAD8BXk9ZV/FOutGquOdbTYvW3wEHS1oTeKJypjb9uvnq39C2a9MLnrU9r1lr90XbJ0qaVTtUtGqN/hFC2w9JWqtmoGiPpPcCRwAvBuZQutRdS5lmGWMqhVHEEJJ2BNZnQUcubP9b1VDt+lPb+zcLT0+XdDbw/dqh2mL7o5L+EXi0eWo4l251pTvF9j39JySNeyeuiZ6WdADwbhZ0KVxqMf99jJ9nJa3b+11o1p117QFBlx1O2arhR7Zf3Wxh8LHKmWLEptQOEDHZSDoD+BywE+UJ0TQW3uy1C55uXh+WtDllAer69eK0S9L+wLymKPoYcCZl1/OuuEDSOr0DSa8CuvRgAOAgYAfgU7bvlrQB5ecguuPvgaskndF8LlwBHFU5U7Tnid6+RZKWtn0rsGnlTDFiab4QMYGk24DN3OFfDkl/RVljsQXwr8CKwNG2T66Zqy19HQl3Ao6jFMp/Z3u7ytFaIWk7yn4tewLbAMcDe2chenSNpDWA7SkdyX5k+7eVI8WISVqymUZ7MWXE+EjKg9IHgRVsv6FqwBipFEYRE0g6DzjU9q9rZ6mh2fF7P9vn1s5Si6RZtreRdBwwx/bZvXO1s7WlKQq/DDwF7Gn7N5UjtaoZJfsHYD3KlNpeq94ubPIaDUkvZsHPAAC2r6iXKEZN0kzbUyecew1l5sS3bT85/E/GOEhhFDGBpEuBrSmbuvZ35Nq7WqiWSbrC9s61c9TSNN24F3gtsC2lCcP1496uW9KFLLyGYgvgV5SW9dh+67A/N46a5ht/Q9n1fn57XtsPVAsVrWrWGb4duBV4tjntLn0WdFHXHoLFwlIYRUzQbO43wPblbWepRdLRlGLg68Dc3nnbD1YL1SJJywNvoIwW3dGst9li3Hd8b56KLpLtS9rKUltvP7PaOaIeSbcDW2aEoFsk/RL4/KKu217ktfjjl650ERN0qQBajN5+RR/qO2egE9OIbD8u6T7KvPI7gHnN61jrFT6S1gXus/1Ec7wcsEbNbBVcKumzwAUsPHI8s16kaNldlE6EKYy6ZQnKutpObV4XRUaMIiaQ9BiDLVkfAW4EjrR9V/up2iVp2d5N8eLOjStJx1A6Eb7U9iaSXgScZ/tVlaO1QtKNwI62n2qOlwGutP3Kusna00ypnci2d2s9TFQh6XxgK+ASFi6OD60WKkZu2Bqj6I6MGEUM+jxlXcXZlCdG7wBeCNwO/Avd2NztGmDiB8Owc+PqLZRubDMBbP9K0kp1I7VqyV5RBGD7yaY46gzbu9bOENVd3HxFt2SkqMNSGEUMesOEtQVflXSt7WMl/V21VC2Q9ELKLt/LSdqGBR8QKwPLVwvWvqdsW5IBJK1QO1DLHpC0h+3vAEjak9KqduxJOtD2mZKOGHY96wu6w/bptTNEFYtdaxnjLYVRxKBnJb0N+EZzvF/ftXGfe/p64D3AS1h48eljwFgXhROcK+lkYFVJf01Zc3VK5Uxt+iBwtqQvN8f3AwdWzNOmXhHcpRHCGELS3Qx5z0/L9vHWlSZDMVzWGEVMIGlD4EuUXe8NXEtp23svsK3tqyrGa4WkfW2fXztHTZJeB+xOGTX7vu0fVI7UOkmrAth+uHaWyUbSUbaPq50jRkfSC/oOlwX2B1a3/fFKkSJixFIYRcSAZj3JvsD6LLyx4bG1MrVF0hKUQui1tbO0TdIBts+RNHRxue0T2s40WWWBdjdJusr2TrVzRMRoZCpdxASSNgFOAta2vbmkLYG9bX+ycrQ2XUTpxDeDjrWqtf2MpMclrWL7kdp5WrZa87pm1RR/HLJAe8xJ6i98p1A6VWaKZcQYy4hRxASSLgc+Apzc2/1a0i22N6+brD1d+/tOJOlcYHvgByy8wW3a9AaQEaMumNCyfR5wN/BPtm+vFCkiRiwjRhGDlrd9vbTQA+F5tcJUco2kLWzPqR2kkm83X50kaQ1Kw4n1WXgq5ftqZZqEMmI0piQdZvtLwNFdWFMaEQukMIoY9FtJG9F0I5K0H/DrupFatxPwnqYr05OUm0Db3rJurHbYPl3S0sCmlJ+D2/v39emAiyhNR64CnqmcZbI6r3aAGJmDKA14TqA7e7dFBJlKFzGg6Ur3VWBH4CHK9Il32v551WAtkrTesPNd+f9A0h7AycBPKUXhBsD7bX+3arCWSLrJ9ta1c9QkaQPgEAZHzfaulSnaIekcSlfSNSnvAfMv0aEHRBFdlMIooo+kKcB+ts9tNvWcYvux2rlqkLQTsLHt0yStCaxo++7audog6SfAnrbvbI43Ar5te9O6ydoh6TjgUtvTa2epRdLNwNeAOcCzvfO2L68WKlrTbHb9fWCgEO7KA6KILkphFDGBpCts71w7R02SjqF0YHqp7U0kvQg4z/arKkdrxcSfAZUFZ5eP+8+FpIcoUwcFrAI8DjzFgiflq1eM1ypJ19nernaOmLwknW9739o5IuIPJ4VRxASSjgZ+B3ydhTuSdWY3bEk3AdsAM/s6883uyhQSSScB6wHnUgqF/YHbgasBbF9QL93oNHs4LZLtzqw3kvTnwMbAdPpa1tueWS1UTCqSZvXeHyNiPKT5QsSgv6TcDB884fyGFbLU8pRtS+o1oFihdqCWLQv8BtilOb4fWB3Yi/KzMZaFUa/wkTTd9u791yRNB3Yf+gfH0xbAu4DdWDCVzs1xBDQNeiJifKQwihi0GaUo2onywXcl8JWqidp3rqSTgVUl/TWlWDylcqbW2D5ocdclHWX7uLbytKXpxLccsLaklVjQknplYN1qwep4C7Bhx7oRRkR0WqbSRUzQbO75KHBWc+oAYFXbb6uXqn2SXkcZIRDwfds/qBxp0hjXzT0l/Q1wBLAWZcSsVxg9Cpxi+4u1srVN0teBQ2zfVztLTE6ZShcxflIYRUwg6WbbWz3XuXHWtCr+te0nmuPlgLVt/6xqsEli3G+IJB2+uCJI0m62f9hmprZJugzYEriBhdcYpV13hzTvfevavn3Itd273LkxYhxlKl3EoFmStrd9LYCk7WgW3XfIeZR9nHqeac5NqxNn0hnrJ0r/i5GhzzH+G18eUztA1CVpL8rP+tLABpK2Bo7tFccpiiLGTwqjiEHbAe+WdE9zvC5wm6Q5dGdzvyX711bYfqpZfxKFnvs/GWtj//fPfkUB/APwSuAyANs3SVq/XpyIGLUURhGD3lA7wCRwv6S9bV8MIOnNwG8rZ5pMzqsdoLKxHjEDkPQYC/6eSwNLAXNtr1wvVbRsnu1HyjZmEdEFKYwiJsiu5gB8ADhL0j9TRgd+Aby7bqTRk3Qii7npt31o8/rp1kJFFbZX6j+WtA9l9CC645ZmP6slJG0MHApcUzlTRIzQlNoBImLysf1T29tTWpdvZntH23fWztWCG4EZlH2MpgJ3NF9bU9ZZRfGL2gHaZvubZA+jrjkEeDml+cbZwCPA4VUTRcRIpStdRAyQtAywL7A+fSPLto+tlalNki4Fdrf9dHO8FDDd9q51k42WpMV2XOtNrewCSW/tO5wCvALYxfYOlSJFJZJWsD23do6IGL1MpYuIYS6iPB2dQV+r4g55EbAS8GBzvGJzbtzt37yuQelKeFlzvAtwOdCZwgjYq+/7ecDPgDfXiRI1SNoROJXy+7+upK2A99s+uG6yiBiVFEYRMcxLbHe5CcVnKG3bL22Od6F0qBprtt8FIOliyhTKe5vjFwMn1MzWJklLALNtf6F2lqjqC8DraR4I2L5Z0s51I0XEKGWNUUQMc42kLWqHqMX2aZS27RcCFwA72D69bqpWbdgrihq/Al5aK0zbbD8DZCPXwPbE9XRZaxgxxjJiFBHD7AS8R9LdlKl0ojt7OPW8Enh1872Bb1XM0rYrJH0bOIfyd38HcEXdSK27punK+HVg/voS2zPrRYqW/aKZTudmH7dDgdsqZ4qIEUrzhYgYIGm9Yee70spc0meAacBZzakDgBttH1UvVXtUNm7ZnwWF4RXAN9yhD4y+aZT9bDud6TpC0hrAl4DXUh4OTQcOs/1A1WARMTIpjCJiqGahce/G+ErbN9fM0yZJs4GtbT/bHC8BzOrYiFlEZzW/84dmnVlEt2SNUUQMkHQYZbRkrebrTEmH1E3VulX7vl+lWooWSXpI0oNDvh6S9OBz/y+MD0lrS/qapO82x5tJem/tXNGOZp1ZuhBGdExGjCJiQDNiskNv7w5JKwA/6sqIiaQDKJ3pLqVModkZOMr2v1cNNmLNU/JFam4WO6EpiE4D/t72VpKWpIwadrYpSddI+hTloUjWmUV0RAqjiBggaQ4wzfYTzfGywA1duimUtA5lnZGA62z/d+VIrZK0OaUJB8AVtn9cM0/bJN1ge5qkWba3ac7dZHvr2tmiHVlnFtE96UoXEcOcBlwn6cLmeB/gaxXz1DCNMlIE8Cwd6kon6cPAwcA3m1PnSfqy7f9XMVbb5kp6AaUrH5K2p2x6HB1he9faGSKiXRkxioihJE2ljBiIMmIwq3Kk1qQrnWYDO9r+n+Z4ReCarkylhPk//ycCmwO3AGsC+9meXTVYtEbSEUNOPwLMsH1T23kiYvQyYhQRA5qn47f25tJLWknSdravqxytLXuwcFe604FZQCcKI0ox/HTf8dPNuS7ZCHgj8CfAvpQNf/OZ2S2vaL56o8VvAm4APiDpPNvHV0sWESORrnQRMcxJwP/0Hc9tznVJ57rS9TkDuFbSxyR9DLgGOL1yprYdbftRYDXKPjZfpXu/A133AmCq7SNtH0kpktakTLF9T81gETEaefoVEcOofzNP2882Xbm64jhgVrP4en5XurqRRk/SurbvsX1883d/NeXv/wHbN1SO17ZeB743AV+xfZGkf6iYJ9q3LvBU3/HTwHq2fyfpyUqZImKEunSjExH/e3dJOpQFT8gPBu6qmKdVts+RdBkLutL93450pbsQ2FbSdNu7U6YNddW9kk6mjBb9o6RlyCyLrjmbMnJ6UXO8F3BOs31Bp7o0RnRFmi9ExABJawEnALtRunJdAhxu+76qwUasWXC/SOO+f4mkm4DzgA8An5143fYJrYeqRNLywBuAObbvaNq3b2F7euVo0SJJ27KgCc1Vtm+sHCkiRiiFUUT83iQdZfu42jn+0CbsW9L/5ig6sH+JpJcBbwU+DJw68brto1sPFdEySSvbflTS6sOu236w7UwR0Y4URhHxe5M00/ZiR1f+mElajjJ9cCdKgXQlcFJvw9txJ2kv24vct0nSgbbPbDNTRFsk/YftPSXdzfAHJBtWihYRI5bCKCJ+b5Jm2d6mdo5RkXQu8CgL72O0qu231Us1eYx7YRwREd2U5gsR8XyM+xOVl9requ/4Ukk3V0sz+XRtT6PokK6vNYzoshRGEfF8jPuN8SxJ29u+FkDSdsDVlTNNJuNeGEe3/VPzuixl76KbKe95WwLXUabYRsQYSmEUEc/HebUDjIKkOZSb/qWAd0u6pzlej7Tn7TfuhXF0mO1dAST9O/A+23Oa482Bv62ZLSJGK4VRRAyQtAllD6O1bW8uaUtgb9ufBLD96aoBR2fP2gH+SFxbO0BECzbtFUUAtm+RtHXNQBExWmm+EBEDJF0OfAQ4uddkQdIttjevmyzaIGlpYB9gffoeoI1xQRwxQNI5wFzgTMrI8YHAirYPqBosIkYmI0YRMczytq+XFpoxNa9WmGjdhcATwAzgmcpZImo5CPggcFhzfAVlJD0ixlQKo4gY5reSNqJZZC9pP+DXdSNFi9bL6GB0ne0nJH0F+I7t22vniYjRm1I7QERMSh8CTgY2lXQvcDjlyWl0w7WSNqsdIqImSXsDNwHfa463lnRx3VQRMUpZYxQRiyRpBWCK7cdqZ4n2NN35NgHuBJ6kdKFzNnWNLpE0A9gNuKxvreVs21vWTRYRo5KpdBExQNJhwGnAY8ApzYaHH7U9vW6yaMk+tQNETALzbD8yYa1lRIyxTKWLiGH+0vajwO7AWpRFyJ+pGylGrRkhBLh/EV8RXXKLpD8HlpC0saQTgWtqh4qI0UlhFBHD9B6R7gGcZvtmsqlnF3yjeb0VuGXIa0SXHAK8nDKd9BzgUcp6y4gYU1ljFBEDJJ0GvBjYANgKWIIyz37bqsGiGklyPjCigyStTFljl7WWEWMuI0YRMcx7gY8C02w/DixNmU4XHSDp4xOOpwCnV4oTUYWkaU0jktnAHEk3S8rDoYgxlsIoIgbYfhZ4CfAxSZ8DdrQ9u3KsaM/Gkj4CIGlpyhS7e+pGimjd14CDba9ve33KNgan1Y0UEaOUqXQRMUDSZ4BpwFnNqQOAG20fVS9VtKUZITobmAG8BrjE9mfrpopol6Srbb/quc5FxPhIYRQRAyTNBrZuRo6QtAQwK/t3jDdJ/f++S1GemF8FfBUgo4bRJZK+ACxPabxg4O3AQ8D5ALZn1ksXEaOQwigiBjSF0Z/ZfrA5Xp3SfCGF0RiTdOViLtv2zq2FiahM0qWLuWzbu7UWJiJakQ1eI2KY44BZzY2BgJ2BTKMbc7ZfXTtDxGRhe9fFXZf0F7bTlCRijGTEKCKGkrQOZZ2RgOts/3flSNESSR8G/s32o5K+AkwFjrJ9SeVoEZOGpJm2p9bOERF/OOlKFxEDJL0FeNz2xbYvAp6QtE/tXNGa9zVF0e6U7oQfBI6vnClissmm1xFjJoVRRAxzjO1Hege2HwaOqZgn2tWbSvBG4DTbM8jnRcREmXITMWbyQRcRwwx7b8iaxO64WdJ3gL2A70pakdwERkyUEaOIMZMbnYgY5kZJnwe+TLkhPoSyp010w0HAtsCdth+XtAbw3t5FSZva/km1dBGTw9W1A0TEH1aaL0TEAEkrAEcDr6U8FZ0OfNL23KrBYlLIovPoAkmHAacBjwGnAtsAH7U9vWqwiBiZFEYREfF7kTTL9ja1c0SMkqSbbW8l6fXAhygPi07LQ4GI8ZWpdBExoNm/aOCpSTY0jEaeqEUX9NYQ7UEpiG6WlHVFEWMshVFEDPO3fd8vC+wLzKuUJSKihhmSpgMbAEdJWgl4tnKmiBihTKWLiP8VSZfb3qV2jqhP0g22p9XOETFKkqYAWwN32X5Y0guAF9ueXTlaRIxI2nVHxABJq/d9rdHMsX9h7VzRDknbS1q++f4AScdL+pPe9RRF0REGNgMObY5XoIygR8SYyohRRAyQdDflpkCUKXR3A8favqpqsGiFpNnAVsAWwFnAvwJ7Z8QwukTSSZSpc7vZfpmk1YDpeTAQMb6yxigiBtjeoHaGqGqebUt6M/Al26dKemftUBEt2872VEmzAGw/JGnp2qEiYnRSGEXEfJLeurjrti9oK0tUNVfSR4B3Abs0ay2Wqpwpom1PS1qCpgujpDVJ84WIsZbCKCL67bWYawZSGHXD24EDgffb/rWkdYHPV84U0bYTgAuBtSR9CtiPspdRRIyprDGKiIgBzdPxaZSC+Ebb91eOFNE6SZsCr6Gst7zE9m2VI0XECKUwiogBko4YcvoRYIbtm9rOE+2SdBBwLHA55YZwJ+Djtk+vGiyiRZLOsP2u5zoXEeMjhVFEDJB0NvAK4FvNqTcBNwCbAufZPr5Wthg9SbcDO/VGiSStAVxt+6V1k0W0R9JM21P7jpcA5tjerGKsiBih7GMUEcO8AJhq+0jbR1KKpDWBnYH31AwWrbgXeLjv+BHgl5WyRLRK0lGSHgO2lPSopMea4/uAiyrHi4gRyohRRAyQdBuwle2nmuNlgJuavTxm2d6mbsIYJUn/CmwOfJOyxmgfyojhTwBsn1AtXERLJB1n+6jaOSKiPelKFxHDnA1cK6n3dHQv4BxJKwA/rhcrWvKL5muZ5vh7zeuadeJEVPH3kg4ENrD9CUl/Aqxj+/rawSJiNDJiFBFDSdqWsuhewFW2b+y7tprth6qFi1ZIWsb2k7VzRNQg6STKvkW7NaPlqwHTbU+rHC0iRiQjRhExlO0ZwIxFXL4EmLqIa/FHTtIrga8BqwDrStoK+Cvbh9RNFtGq7WxPlTQLwPZDkpauHSoiRifNFyLi+VDtADFSJwB7Ag8A2L4Z2LVqooj2Pd10ojPM39vr2bqRImKUUhhFxPORObjjbYrtn08490yVJBH1nABcCKwt6VPAVcCn60aKiFHKVLqIiJjoF810OjdPzA8B/qtypohW2T5L0gzgNc2pfWzfVjNTRIxWRowi4vnIVLrx9kHgCGBd4DfA9s25iK5ZHliCcr+0XOUsETFi6UoXEYskaS1g2d6x7Xua86vbfrBasIiIEZP0cWB/4HzKw6B9gPNsf7JqsIgYmRRGETFA0t7APwEvouz2vh5wm+2XVw0WrZD0NeBI2w83x6sBx9v+67rJItrTbHS9je0nmuPlgJm2X1Y3WUSMSqbSRcQwn6BMn/ov2xtQ5thfXTdStGhqryiC0qYY2LZinogafkbfiDllw+Of1okSEW1I84WIGOZp2w9ImiJpiu1LJf1j7VDRmimSVrH9CMwfMVqqcqaIVkg6kdJ580ngVkk/aI5fR+lMFxFjKoVRRAzzsKQVgSuAsyTdB8yrnCna80XgR5K+TrkhfAdwfN1IEa25sXmdQWnX3XNZ+1Eiok1ZYxQRAyStADxBWXD8TmAV4CzbD1QNFq2RtCWwG+Vn4D9tz6kcKSIiYqRSGEXEIklamb6R5XSiG2+SVrA9t/l3H2D70bYzRdQiaWPgOGAzFu7OuWG1UBExUplKFxEDJL0fOBb4HfAsZdTAQG4Ixts3gDcCt1L+vXt6//7r1ggVUclpwDHAF4BdgYPIHm4RYy0jRhExQNIdwA62f1s7S7RLkoB1bP+qdpaImiTNsL2tpDm2t2jOXWn71bWzRcRoZMQoIob5KfB47RDRPtuW9C3SnjviCUlTgDskfRi4F1ircqaIGKGMGEXEAEnbUKaRXEdpWQuA7UOrhYrWSDoJOMX2zNpZImqRNA24DViVsrfbKpSNjq+tGiwiRiaFUUQMkHQ9Zb+OOZQ1RgDYPr1aqBg5SUvanidpDvAyysjhXJo1RranVg0YERExQplKFxHDzLN9RO0Q0brrganAPrWDRNQi6Yu2D2+mlA48Pba9d4VYEdGCFEYRMcylkt4HfIuFp9KlXfd4E4Dtn9YOElHRGc3r56qmiIjWZSpdRAyQdPeQ087+HeNN0i+Bzy/quu1FXosYR5LWBLB9f+0sETF6GTGKiAG2N6idIapYAliR7NUSHda0rD8G+DDld2GKpHnAibaPrRouIkYqI0YRMUDS/sD3bD8m6WOUdSefsD2rcrQYIUkz02Ahuk7S3wB7AO+zfXdzbkPgJMr74hdq5ouI0ZlSO0BETEpHN0XRTsDrgdOBr1TOFKOXkaIIeDdwQK8oArB9F3Bgcy0ixlQKo4gY5pnm9U3ASbYvApaumCfa8ZraASImgaVs/3biyWad0VIV8kRES1IYRcQw90o6GXgb8B1Jy5D3i7GXroMRADz1PK9FxB+5rDGKiAGSlgfeAMyxfYekdYAtbE+vHC0iYqQkPUPZ2HjgErCs7YwaRYypFEYRsUiS1gKW7R3bvqdinIiIiIiRydSYiBggaW9JdwB3A5c3r9+tmyoiIiJidFIYRcQwnwC2B/6r2dPotcDVdSNFREREjE4Ko4gY5mnbD1A2Npxi+1Jg69qhIiIiIkZlydoBImJSeljSisAVwFmS7gPmVc4UERERMTJpvhARAyStADxB6cL0TmAV4KxmFCkiIiJi7KQwioiIiIiIzstUuoiYT9JjgCkjRTTf0xz3y8SpAAAAxElEQVTb9spVgkVERESMWEaMIiIiIiKi8zJiFBHzSVoW+ADwp8Bs4F9sp+lCREREjL2MGEXEfJK+DjwNXAm8Efi57cPqpoqIiIgYvRRGETGfpDm2t2i+XxK43vbUyrEiIiIiRi4bvEZEv6d732QKXURERHRJRowiYj5JzwBze4fAcsDjpCtdREREjLkURhERERER0XmZShcREREREZ2XwigiIiIiIjovhVFERERERHReCqOIiIiIiOi8FEYREREREdF5/x/aIX2hE334ngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols=df.columns\n",
    "feat_corr = df.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(feat_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(feat_corr, mask=feat_corr < 1, cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants and Age = 0.54\n"
     ]
    }
   ],
   "source": [
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = feat_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (feat_corr.iloc[i,j] >= threshold and feat_corr.iloc[i,j] < 1) or (feat_corr.iloc[i,j] < 0 and feat_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([feat_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                         0\n",
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "Diabetes_pedigree_function        0\n",
      "Age                               0\n",
      "Target                            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "df[NaN_col_names] = df[NaN_col_names].replace(0, np.NaN)\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>Triceps_skin_fold_thickness_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Triceps_skin_fold_thickness  Triceps_skin_fold_thickness_Missing\n",
       "0                         35.0                                    0\n",
       "1                         29.0                                    0\n",
       "2                          NaN                                    1\n",
       "3                         23.0                                    0\n",
       "4                         35.0                                    0\n",
       "5                          NaN                                    1\n",
       "6                         32.0                                    0\n",
       "7                          NaN                                    1\n",
       "8                         45.0                                    0\n",
       "9                          NaN                                    1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Triceps_skin_fold_thickness_Missing'] = df['Triceps_skin_fold_thickness'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "df[['Triceps_skin_fold_thickness','Triceps_skin_fold_thickness_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dc2ea55400>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline\n",
    "sns.countplot(x=\"Triceps_skin_fold_thickness_Missing\", hue=\"Target\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dc2ef92048>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['serum_insulin_Missing'] = df['serum_insulin'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "sns.countplot(x=\"serum_insulin_Missing\", hue=\"Target\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"Triceps_skin_fold_thickness_Missing\", \"serum_insulin_Missing\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                              0\n",
      "Plasma_glucose_concentration           0\n",
      "blood_pressure                         0\n",
      "Triceps_skin_fold_thickness            0\n",
      "serum_insulin                          0\n",
      "BMI                                    0\n",
      "Diabetes_pedigree_function             0\n",
      "Age                                    0\n",
      "Target                                 0\n",
      "Triceps_skin_fold_thickness_Missing    0\n",
      "serum_insulin_Missing                  0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "medians = df.median() \n",
    "df =df.fillna(medians)\n",
    "\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:462: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "y_train = df['Target']   \n",
    "X_train =df.drop([\"Target\"], axis=1)\n",
    "\n",
    "#用于保存特征工程之后的结果\n",
    "feat_names = X_train.columns\n",
    "\n",
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.DataFrame(columns = feat_names, data = X_train)\n",
    "\n",
    "df = pd.concat([X_train, y_train], axis = 1)\n",
    "\n",
    "df.to_csv(\"E:\\\\Jupyter\\\\TZ_pima-indians-diabetes.csv\",index = False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "      <th>Triceps_skin_fold_thickness_Missing</th>\n",
       "      <th>serum_insulin_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                         148.0            72.0   \n",
       "1          1                          85.0            66.0   \n",
       "2          8                         183.0            64.0   \n",
       "3          1                          89.0            66.0   \n",
       "4          0                         137.0            40.0   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                         35.0          125.0  33.6   \n",
       "1                         29.0          125.0  26.6   \n",
       "2                         29.0          125.0  23.3   \n",
       "3                         23.0           94.0  28.1   \n",
       "4                         35.0          168.0  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \\\n",
       "0                       0.627   50       1   \n",
       "1                       0.351   31       0   \n",
       "2                       0.672   32       1   \n",
       "3                       0.167   21       0   \n",
       "4                       2.288   33       1   \n",
       "\n",
       "   Triceps_skin_fold_thickness_Missing  serum_insulin_Missing  \n",
       "0                                    0                      1  \n",
       "1                                    0                      1  \n",
       "2                                    1                      1  \n",
       "3                                    0                      0  \n",
       "4                                    0                      0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-0404535af3ce>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Target'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mX_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m \u001b[1;34m\"Target\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mfeat_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'train' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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